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Talari P, N B, Kaur G, Alshahrani H, Al Reshan MS, Sulaiman A, Shaikh A. Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2. PLoS One 2024; 19:e0292100. [PMID: 38236900 PMCID: PMC10796060 DOI: 10.1371/journal.pone.0292100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/12/2023] [Indexed: 01/22/2024] Open
Abstract
Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model's first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system's result is to enhance the classifier's performance in spotting illness early.
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Affiliation(s)
- Praveen Talari
- Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad, India
| | - Bharathiraja N
- Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Rajpura, India
| | - Gaganpreet Kaur
- Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Rajpura, India
| | - Hani Alshahrani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Mana Saleh Al Reshan
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
- Scientific and Engineering Research Centre, Najran University, Najran, Saudi Arabia
| | - Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
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Li L, Cheng Y, Ji W, Liu M, Hu Z, Yang Y, Wang Y, Zhou Y. Machine learning for predicting diabetes risk in western China adults. Diabetol Metab Syndr 2023; 15:165. [PMID: 37501094 PMCID: PMC10373320 DOI: 10.1186/s13098-023-01112-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/15/2023] [Indexed: 07/29/2023] Open
Abstract
OBJECTIVE Diabetes mellitus is a global epidemic disease. Long-time exposure of patients to hyperglycemia can lead to various type of chronic tissue damage. Early diagnosis of and screening for diabetes are crucial to population health. METHODS We collected the national physical examination data in Xinjiang, China, in 2020 (a total of more than 4 million people). Three types of physical examination indices were analyzed: questionnaire, routine physical examination and laboratory values. Integrated learning, deep learning and logistic regression methods were used to establish a risk model for type-2 diabetes mellitus. In addition, to improve the convenience and flexibility of the model, a diabetes risk score card was established based on logistic regression to assess the risk of the population. RESULTS An XGBoost-based risk prediction model outperformed the other five risk assessment algorithms. The AUC of the model was 0.9122. Based on the feature importance ranking map, we found that hypertension, fasting blood glucose, age, coronary heart disease, ethnicity, parental diabetes mellitus, triglycerides, waist circumference, total cholesterol, and body mass index were the most important features of the risk prediction model for type-2 diabetes. CONCLUSIONS This study established a diabetes risk assessment model based on multiple ethnicities, a large sample and many indices, and classified the diabetes risk of the population, thus providing a new forecast tool for the screening of patients and providing information on diabetes prevention for healthy populations.
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Affiliation(s)
- Lin Li
- Zhongshan School of Medicine, Sun Yat-sen University, No. 74, Zhongshan Second Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Yinlin Cheng
- Zhongshan School of Medicine, Sun Yat-sen University, No. 74, Zhongshan Second Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Weidong Ji
- Zhongshan School of Medicine, Sun Yat-sen University, No. 74, Zhongshan Second Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Mimi Liu
- Zhongshan School of Medicine, Sun Yat-sen University, No. 74, Zhongshan Second Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Zhensheng Hu
- Zhongshan School of Medicine, Sun Yat-sen University, No. 74, Zhongshan Second Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Yining Yang
- People's Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Tianshan District, Urumqi, 830001, Xijiang, China.
| | - Yushan Wang
- Center of Health Management, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Xinshi District, Urumqi, 830054, Xinjiang, China.
| | - Yi Zhou
- Zhongshan School of Medicine, Sun Yat-sen University, No. 74, Zhongshan Second Road, Yuexiu District, Guangzhou, 510080, Guangdong, China.
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García-Domínguez A, Galván-Tejada CE, Magallanes-Quintanar R, Gamboa-Rosales H, Curiel IG, Peralta-Romero J, Cruz M. Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation. J Diabetes Res 2023; 2023:9713905. [PMID: 37404324 PMCID: PMC10317588 DOI: 10.1155/2023/9713905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/08/2023] [Accepted: 06/18/2023] [Indexed: 07/06/2023] Open
Abstract
The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. The performance of these models heavily relies on the selection of the classifier algorithm and the quality of the dataset. Therefore, optimizing the input data by selecting relevant features becomes essential for accurate classification. This research presents a comprehensive investigation into diabetes detection models by integrating two feature selection techniques: the Akaike information criterion and genetic algorithms. These techniques are combined with six prominent classifier algorithms, including support vector machine, random forest, k-nearest neighbor, gradient boosting, extra trees, and naive Bayes. By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. The results demonstrate superior performance, surpassing accuracies of 94%. Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. By judiciously selecting relevant features, this approach contributes to the advancement of medical diagnostic capabilities and empowers healthcare professionals in making informed decisions regarding diabetes diagnosis and treatment.
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Affiliation(s)
- Antonio García-Domínguez
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Carlos E. Galván-Tejada
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Rafael Magallanes-Quintanar
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Hamurabi Gamboa-Rosales
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Irma González Curiel
- Academic Unit of Chemical Sciences, Autonomous University of Zacatecas, Juarez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Jesús Peralta-Romero
- Medical Research Unit in Biochemistry, Specialties Hospital, National Medical Center Siglo XXI, Mexican Social Security Institute, Mexico City, Mexico
| | - Miguel Cruz
- Medical Research Unit in Biochemistry, Specialties Hospital, National Medical Center Siglo XXI, Mexican Social Security Institute, Mexico City, Mexico
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Multiview Deep Forest for Overall Survival Prediction in Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023; 2023:7931321. [PMID: 36714327 PMCID: PMC9876666 DOI: 10.1155/2023/7931321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/16/2022] [Accepted: 01/03/2023] [Indexed: 01/19/2023]
Abstract
Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper. MVDF can learn the features of each view and fuse them with integrated learning and multiple kernel learning. Then, a gradient boost forest based on the information bottleneck theory is proposed to reduce redundant information and avoid overfitting. In addition, a pruning strategy for a cascaded forest is used to limit the impact of outlier data. Comprehensive experiments have been carried out on a data set from West China Hospital of Sichuan University and two public data sets. Results have demonstrated that our method outperforms the compared methods in predicting overall survival.
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Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr 2022; 14:196. [PMID: 36572938 PMCID: PMC9793536 DOI: 10.1186/s13098-022-00969-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022] Open
Abstract
Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.
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Sabharwal R, Miah SJ, Fosso Wamba S. Extending artificial intelligence research in the clinical domain: a theoretical perspective. ANNALS OF OPERATIONS RESEARCH 2022:1-32. [PMID: 36407943 PMCID: PMC9641309 DOI: 10.1007/s10479-022-05035-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.
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Affiliation(s)
- Renu Sabharwal
- Newcastle Business School, The University of Newcastle, Callaghan, NSW Australia
| | - Shah J. Miah
- Newcastle Business School, The University of Newcastle, Callaghan, NSW Australia
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A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1167494. [PMID: 36210997 PMCID: PMC9534609 DOI: 10.1155/2022/1167494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 08/29/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022]
Abstract
With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset of features. Moreover, an attention-based convolutional neural network (ACNN) with long short-term memory (LSTM), called (ACNN-LSTM) model, is utilized for the detection of HD by using medical data. An extensive experimental study is performed to ensure the improved classification performance of the FSHDL-HDDC technique. A detailed comparison study reported the betterment of the FSHDL-HDDC method on existing techniques interms of different performance measures. The suggested system, the FSHDL-HDDC, has reached its maximum level of accuracy, which is 0.9772.
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Lin Y, Li Y, Huang X, Liu L, Wei H, Zou X. Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4755728. [PMID: 35795745 PMCID: PMC9252631 DOI: 10.1155/2022/4755728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/19/2022] [Accepted: 05/24/2022] [Indexed: 11/18/2022]
Abstract
At present, diabetes is one of the most important chronic noncommunicable diseases, that have threatened human health. By 2020, the number of diabetic patients worldwide has reached 425 million. This amazing number has attracted the great attention of various countries. With the progress of computing technology, many mathematical models and intelligent algorithms have been applied in different fields of health care. 822 subjects were selected in this paper. They were divided into 389 diabetic patients and 423 nondiabetic patients. Each of the subjects included 41 indicators. Too many indicator variables would increase the computational effort and there could be a strong correlation and data redundancy between the data. Therefore, the sample features were first dimensionally reduced to generate seven new features in the new space, retaining up to 99.9% of the valid information from the original data. A diagnostic and classification model for diabetes clinical data based on recurrent neural networks were constructed, and particle swarm optimization (PSO) was introduced to optimise recurrent neural network's hyperparameters to achieve effective diagnosis and classification of diabetes.
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Affiliation(s)
- Yuanyuan Lin
- Department of Endocrinology, First People's Hospital of Nanning, Nanning 530021, China
| | - Yueli Li
- Department of Endocrinology, First People's Hospital of Nanning, Nanning 530021, China
| | - Xuemei Huang
- Department of Endocrinology, First People's Hospital of Nanning, Nanning 530021, China
| | - Li Liu
- Department of Endocrinology, First People's Hospital of Nanning, Nanning 530021, China
| | - Haitao Wei
- Department of Endocrinology, First People's Hospital of Nanning, Nanning 530021, China
| | - Xinyu Zou
- Department of Endocrinology, First People's Hospital of Nanning, Nanning 530021, China
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Gomes MAS, Kovaleski JL, Pagani RN, da Silva VL. Machine learning applied to healthcare: a conceptual review. J Med Eng Technol 2022; 46:608-616. [PMID: 35678368 DOI: 10.1080/03091902.2022.2080885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The technological inference in procedures applied to healthcare is frequently investigated in order to understand the real contribution to decision-making and clinical improvement. In this context, the theoretical field of machine learning has suitably presented itself. The objective of this research is to identify the main machine learning algorithms used in healthcare through the methodology of a systematic literature review. Considering the time frame of the last twenty years, 173 studies were mined based on established criteria, which allowed the grouping of algorithms into typologies. Supervised Learning, Unsupervised Learning, and Deep Learning were the groups derived from the studies mined, establishing 59 works employed. We expect that this research will stimulate investigations towards machine learning applications in healthcare.
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Affiliation(s)
| | - João Luiz Kovaleski
- Department of Production Engineering, Federal University of Technology of Paraná, Ponta Grossa, Brazil
| | - Regina Negri Pagani
- Department of Production Engineering, Federal University of Technology of Paraná, Ponta Grossa, Brazil
| | - Vander Luiz da Silva
- Department of Production Engineering, Federal University of Technology of Paraná, Ponta Grossa, Brazil
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Han J, Xiao N, Yang W, Luo S, Zhao J, Qiang Y, Chaudhary S, Zhao J. MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data. Int J Comput Assist Radiol Surg 2022; 17:1049-1057. [PMID: 35445285 PMCID: PMC9020752 DOI: 10.1007/s11548-022-02625-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/24/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly. METHODS In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results. RESULTS The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor. CONCLUSION In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.
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Affiliation(s)
- Jiahao Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ning Xiao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wanting Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shichao Luo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jun Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Suman Chaudhary
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
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Javid I, Zager Alsaedi AK, Ghazali R, Mohmad Hassim YM, Zulqarnain M. Optimally organized GRU-deep learning model with Chi2 feature selection for heart disease prediction. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In previous studies, various machine-driven decision support systems based on recurrent neural networks (RNN) were ordinarily projected for the detection of cardiovascular disease. However, the majority of these approaches are restricted to feature preprocessing. In this paper, we concentrate on both, including, feature refinement and the removal of the predictive model’s problems, e.g., underfitting and overfitting. By evading overfitting and underfitting, the model will demonstrate good enactment on equally the training and testing datasets. Overfitting the training data is often triggered by inadequate network configuration and inappropriate features. We advocate using Chi2 statistical model to remove irrelevant features when searching for the best-configured gated recurrent unit (GRU) using an exhaustive search strategy. The suggested hybrid technique, called Chi2 GRU, is tested against traditional ANN and GRU models, as well as different progressive machine learning models and antecedently revealed strategies for cardiopathy prediction. The prediction accuracy of proposed model is 92.17%. In contrast to formerly stated approaches, the obtained outcomes are promising. The study’s results indicate that medical practitioner will use the proposed diagnostic method to reliably predict heart disease.
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Affiliation(s)
- Irfan Javid
- Faculty of Science Computer and Information Technology, Universiti Tun Hussein Onn, Malaysia
- Department of Computer Science and Information Technology, University of Poonch, Rawalakot, AJK, Pakistan
| | | | - Rozaida Ghazali
- Faculty of Science Computer and Information Technology, Universiti Tun Hussein Onn, Malaysia
| | | | - Muhammad Zulqarnain
- Riphah College of Computing, Riphah International University Faisalabad Campus, Pakistan
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Sheeba A, Padmakala S, Subasini CA, Karuppiah SP. MKELM: Mixed Kernel Extreme Learning Machine using BMDA optimization for web services based heart disease prediction in smart healthcare. Comput Methods Biomech Biomed Engin 2022; 25:1180-1194. [PMID: 35174762 DOI: 10.1080/10255842.2022.2034795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart disease is important to prevent cardiac patients prior to heart attack. The main drawback of heart disease is delay in identifying the disease in the early stage. This objective is obtained by using the machine learning method with rich healthcare information on heart diseases. In this paper, the smart healthcare method is proposed for the prediction of heart disease using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA-MKELM) approach. Here, data is gathered from the two devices such as sensor nodes as well as the electronic medical records. The android based design is utilized to gather the patient data and the reliable cloud-based scheme for the data storage. For further evaluation for the prediction of heart disease, data are gathered from cloud computing services. At last, BMDA-MKELM based prediction scheme is capable to classify cardiovascular diseases. In addition to this, the proposed prediction scheme is compared with another method with respect to measures such as accuracy, precision, specificity, and sensitivity. The experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.
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Affiliation(s)
- Adlin Sheeba
- Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Chennai, India
| | - S Padmakala
- Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Chennai, India
| | - C A Subasini
- Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Chennai, India
| | - S P Karuppiah
- Department of MBA, St. Joseph's College of Engineering, Chennai, India
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Fu X, Wang Y, Cates RS, Li N, Liu J, Ke D, Liu J, Liu H, Yan S. Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes. Front Endocrinol (Lausanne) 2022; 13:1061507. [PMID: 36743935 PMCID: PMC9895792 DOI: 10.3389/fendo.2022.1061507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/30/2022] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurement serves as a good source of data to train machine learning models for prediction purposes. This study aimed at using machine learning methods to predict blood glucose for type 2 diabetic patients. We investigated various parameters influencing blood glucose, as well as determined the most effective machine learning algorithm in predicting blood glucose. PATIENTS AND METHODS 273 patients were recruited in this research. Several parameters such as age, diet, family history, BMI, alcohol intake, smoking status et al were analyzed. Patients who had glycosylated hemoglobin less than 6.5% after 52 weeks were considered as having achieved glycemic control and the rest as not achieving it. Five machine learning methods (KNN algorithm, logistic regression algorithm, random forest algorithm, support vector machine, and XGBoost algorithm) were compared to evaluate their performances in prediction accuracy. R 3.6.3 and Python 3.12 were used in data analysis. RESULTS The statistical variables for which p< 0.05 was obtained were BMI, pulse, Na, Cl, AKP. Compared with the other four algorithms, XGBoost algorithm has the highest accuracy (Accuracy=99.54% in training set and 78.18% in testing set) and AUC values (1.0 in training set and 0.68 in testing set), thus it is recommended to be used for prediction in clinical practice. CONCLUSION When it comes to future blood glucose level prediction using machine learning methods, XGBoost algorithm scores the highest in effectiveness. This algorithm could be applied to assist clinical decision making, as well as guide the lifestyle of diabetic patients, in pursuit of minimizing risks of hyperglycemic or hypoglycemic events.
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Affiliation(s)
- Xiaomin Fu
- Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yuhan Wang
- Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ryan S. Cates
- Department of Emergency Medicine Stanford Healthcare TriValley, Stanford University School of Medicine, Stanford, Pleasanton, CA, United States
| | - Nan Li
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Jing Liu
- Clinics of Cadre, Department of Outpatient, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Dianshan Ke
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
| | - Jinghua Liu
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Hongzhou Liu
- Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- *Correspondence: Hongzhou Liu, ; Shuangtong Yan,
| | - Shuangtong Yan
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
- *Correspondence: Hongzhou Liu, ; Shuangtong Yan,
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Fregoso-Aparicio L, Noguez J, Montesinos L, García-García JA. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetol Metab Syndr 2021; 13:148. [PMID: 34930452 PMCID: PMC8686642 DOI: 10.1186/s13098-021-00767-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022] Open
Abstract
Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.
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Affiliation(s)
- Luis Fregoso-Aparicio
- School of Engineering and Sciences, Tecnologico de Monterrey, Av Lago de Guadalupe KM 3.5, Margarita Maza de Juarez, 52926 Cd Lopez Mateos, Mexico
| | - Julieta Noguez
- School of Engineering and Sciences, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849 Monterrey, Nuevo Leon Mexico
| | - Luis Montesinos
- School of Engineering and Sciences, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849 Monterrey, Nuevo Leon Mexico
| | - José A. García-García
- Hospital General de Mexico Dr. Eduardo Liceaga, Dr. Balmis 148, Doctores, Cuauhtemoc, 06720 Mexico City, Mexico
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15
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Abstract
This paper presents a Laboratory Virtual Instrument Engineering Workbench (LabVIEW) and Internet of Things (IoT)-based eHealth monitoring system called LI-Care to facilitate the diagnosis of the health condition cost-effectively. The system measures the heart rate, body temperature, blood pressure, oxygen level, and breathing rate, and provides an electrocardiogram (ECG). The required sensors are integrated on a web-based application that keeps track of the essential parameters and gives an alarm indication if one or more physiological parameters go beyond the safe level. It also employs a webcam to obtain the patient view at any time. LabVIEW enables the effortless interfacing of various biomedical sensors with the computer and provides high-speed data acquisition and interactive visualizations. It also provides a web publishing tool to access the interactive window remotely through a web browser. The web-based application is accessible to doctors who are experts in that particular field. They can obtain the real-time reading and directly perform a diagnosis. The parameters measured by the proposed system were validated using the traditional measurement systems, and the Root Mean Square (RMS) errors were obtained for the various parameters. The maximum RMS error as a percentage was 0.159%, which was found in the temperature measurement, and its power consumption is 1 Watt/h. The other RMS errors were 0.05% in measurement of systolic pressure, 0.029% in measurement of diastolic pressure, 0.059% in measurement of breathing rate, 0.002% in measurement of heart rate, 0.076% in measurement of oxygen level, and 0.015% in measurement of ECG. The low RMS errors and ease of deployment make it an attractive alternative for traditional monitoring systems. The proposed system has potential applications in hospitals, nursing homes, remote monitoring of the elderly, non-contact monitoring, etc.
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16
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Haq AU, Li JP, Ahmad S, Khan S, Alshara MA, Alotaibi RM. Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare. SENSORS (BASEL, SWITZERLAND) 2021; 21:8219. [PMID: 34960313 PMCID: PMC8707954 DOI: 10.3390/s21248219] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/25/2021] [Accepted: 11/30/2021] [Indexed: 01/15/2023]
Abstract
COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.
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Affiliation(s)
- Amin Ul Haq
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Jian Ping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Sultan Ahmad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia;
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.A.); (R.M.A.)
| | - Mohammed Ali Alshara
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.A.); (R.M.A.)
| | - Reemiah Muneer Alotaibi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.A.); (R.M.A.)
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17
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Navaz AN, Serhani MA, El Kassabi HT, Al-Qirim N, Ismail H. Trends, Technologies, and Key Challenges in Smart and Connected Healthcare. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:74044-74067. [PMID: 34812394 PMCID: PMC8545204 DOI: 10.1109/access.2021.3079217] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/05/2021] [Indexed: 05/04/2023]
Abstract
Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.
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Affiliation(s)
- Alramzana Nujum Navaz
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Mohamed Adel Serhani
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Hadeel T. El Kassabi
- Department of Computer Science and Software EngineeringCollege of Information TechnologyUAE UniversityAl AinUnited Arab Emirates
| | - Nabeel Al-Qirim
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Heba Ismail
- Department of Computer Science and Information Technology (CS-IT)College of EngineeringAbu Dhabi UniversityAl AinUnited Arab Emirates
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18
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Nedyalkova M, Madurga S, Simeonov V. Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041919. [PMID: 33671157 PMCID: PMC7922378 DOI: 10.3390/ijerph18041919] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/06/2021] [Accepted: 02/10/2021] [Indexed: 02/06/2023]
Abstract
A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polyneuropathy (DPNP), and diabetic microangiopathy (DMA)). Clustering is a machine learning tool for discovering structures in datasets. Clustering has been proven to be efficient for pattern recognition based on clinical records. The considered combinatorial k-means procedure explores all possible k-means clustering with a determined number of descriptors and groups. The predetermined conditions for the partitioning were as follows: every single group of patients included patients with DMT2 and one of the underlying diseases; each subgroup formed in such a way was subject to partitioning into three patterns (good health status, medium health status, and degenerated health status); optimal descriptors for each disease and groups. The selection of the best clustering is obtained through the parameter called global variance, defined as the sum of all variance values of all clinical variables of all the clusters. The best clinical parameters are found by minimizing this global variance. This methodology has to identify a set of variables that are assumed to separate each underlying disease efficiently in three different subgroups of patients. The hierarchical clustering obtained for these four underlying diseases could be used to build groups of patients with correlated clinical data. The proposed methodology gives surmised results from complex data based on a relationship with the health status of the group and draws a picture of the prediction rate of the ongoing health status.
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Affiliation(s)
- Miroslava Nedyalkova
- Department of Chemistry, University of Fribourg, Chemin du Musée 9, 1700 Fribourg, Switzerland
- Correspondence:
| | - Sergio Madurga
- Materials Science and Physical Chemistry Department, Research Institute of Theoretical and Computational Chemistry (IQTCUB), University of Barcelona, C/Martí i Franquès, 08028 Barcelona, Spain;
| | - Vasil Simeonov
- Department of Analytical Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia “St. Kl. Okhridski”, 1, J. Bourchier Blvd., 1164 Sofia, Bulgaria;
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19
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Xiao H, Ali S, Zhang Z, Sarfraz MS, Zhang F, Faisal M. Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6635463. [PMID: 33604008 PMCID: PMC7868142 DOI: 10.1155/2021/6635463] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/09/2021] [Accepted: 01/20/2021] [Indexed: 11/23/2022]
Abstract
Healthcare system facilitates the treatment of patients with the support of wearable, smart, and handheld devices, as well as many other devices. These devices are producing a huge bulk of data that need to be moulded for extracting meaningful insights from them for the useful use of researchers and practitioners. Various approaches, methods, and tools are in use for doing so and to extract meaningful information in the field of healthcare. This information is being used as evidence to further analyze the data for the early care of patient and to devise treatment. Early care and treatment can facilitate healthcare and the treatment of the patient and can have immense potentiality of dropping the care cost and quality refining of care and can decrease waste and chances of error. To facilitate healthcare in general and cardiology in specific, the proposed study presents an overview of the available literature associated with big data, its insights, and analytics. The presented report will help practitioners and researchers to devise new solutions for early care in healthcare and in cardiology.
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Affiliation(s)
- Hui Xiao
- Zhongnan Hospital of Wuhan University, Information Center, Wuhan 430071, China
| | - Sikandar Ali
- Department of Computer Science and Technology, China University of Petroleum-Beijing, Beijing 102249, China
| | - Zhen Zhang
- Zhongnan Hospital of Wuhan University, Information Center, Wuhan 430071, China
| | - Muhammad Shahzad Sarfraz
- Department of Computer Science, National University of Computer and Emerging Sciences Islamabad, Chiniot-Faisalabad Campus, Chiniot, Pakistan
| | - Fang Zhang
- Zhongnan Hospital of Wuhan University, Information Center, Wuhan 430071, China
| | - Mohammad Faisal
- Department of Computer Science and Information Technology, University of Malakand, Chakdara, Pakistan
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20
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Khan J, Li JP, Haq AU, Khan GA, Ahmad S, Abdullah Alghamdi A, Golilarz NA. Efficient secure surveillance on smart healthcare IoT system through cosine-transform encryption. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201770] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The emerging technologies with IoT (Internet of Things) systems are elevated as a prototype and combination of the smart connectivity ecosystem. These ecosystems are appropriately connected in a smart healthcare system which are generating finest monitoring activities among the patients, well-organized diagnosis process, intensive support and care against the traditional healthcare operations. But facilitating these highly technological adaptations, the preserving personal information of the patients are on the risk with data leakage and privacy theft in the current revolution. Concerning secure protection and privacy theft of the patient’s information. We emphasized this paper on secure monitoring with the help of intelligently recorded summary’s keyframe extraction and applied two rounds lightweight cosine-transform encryption. This article includes firstly, a regimented process of keyframe extraction which is employed to retrieve meaningful frames of image through visual sensor with sending alert (quick notice) to authority. Secondly, employed two rounds of lightweight cosine-transform encryption operation of agreed (detected) keyframes to endure security and safety for the further any kinds of attacks from the adversary. The combined methodology corroborates highly usefulness with engendering appropriate results, little execution of encryption time (0.2277-0.2607), information entropy (7.9996), correlation coefficient (0.0010), robustness (NPCR 99.6383, UACI 33.3516), uniform histogram deviation (R 0.0359, G 0.0492, B 0.0582) and other well adopted secure ideology than any other keyframe or image encryption approaches. Furthermore, this incorporating method can effectively reduce vital communication cost, bandwidth issues, storage, data transmission cost and effective timely judicious analysis over the occurred activities and keep protection by using effective encryption methodology to remain attack free from any attacker or adversary, and provide confidentiality about patient’s privacy in the smart healthcare system.
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Affiliation(s)
- Jalaluddin Khan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Ping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Amin Ul Haq
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Ghufran Ahmad Khan
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
| | - Sultan Ahmad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | | | - Noorbakhsh Amiri Golilarz
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
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21
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Tahir A, Chen F, Khan HU, Ming Z, Ahmad A, Nazir S, Shafiq M. A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems. SENSORS 2020; 20:s20185392. [PMID: 32967094 PMCID: PMC7570508 DOI: 10.3390/s20185392] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/12/2020] [Accepted: 09/14/2020] [Indexed: 02/07/2023]
Abstract
As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.
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Affiliation(s)
- Adnan Tahir
- Research Institute of Network and Information Security, Shenzhen University, Shenzhen 518060, China; (A.T.); (F.C.)
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Fei Chen
- Research Institute of Network and Information Security, Shenzhen University, Shenzhen 518060, China; (A.T.); (F.C.)
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Habib Ullah Khan
- Department of Accounting and Information System, College of Business and Economics, Qatar University, Doha P.O. Box. 2713, Qatar
- Correspondence:
| | - Zhong Ming
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Arshad Ahmad
- Department of Computer Science, City University of Science and Information Technology, Peshawar 25000, Pakistan;
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Ambar 23430, Pakistan;
| | - Muhammad Shafiq
- Cyberspace Institute of Technology, Guangzhou University, Guangzhou 510006, China;
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22
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An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:5363549. [PMID: 32879636 PMCID: PMC7448109 DOI: 10.1155/2020/5363549] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 06/11/2020] [Indexed: 02/08/2023]
Abstract
Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisions by associating some relevant disease indicators and combining them with medical images. Through data collection, analysis and integration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliary diagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor's diagnosis rate will be reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor's expertise. Therefore, intelligent systems can help doctors and medical experts treat patients more effectively.
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23
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Slaninova N, Fiedorova K, Selamat A, Danisova K, Kubicek J, Tkacz E, Augustynek M. Analysis and Testing of a Suitable Compatible Electrode's Material for Continuous Measurement of Glucose Concentration. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20133666. [PMID: 32629993 PMCID: PMC7374362 DOI: 10.3390/s20133666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 06/27/2020] [Accepted: 06/28/2020] [Indexed: 06/11/2023]
Abstract
The subject of the submitted work is the proposal of electrodes for the continual measurement of the glucose concentration for the purpose of specifying further hemodynamic parameters. The proposal includes the design of the electronic measuring system, the construction of the electrodes themselves and the functionality of the entire system, verified experimentally using various electrode materials. The proposed circuit works on the basis of micro-ammeter measuring the size of the flowing electric current and the electrochemical measurement method is used for specifying the glucose concentration. The electrode system is comprised of two electrodes embedded in a silicon tube. The solution consists of the measurement with three types of materials, which are verified by using three solutions with a precisely given concentration of glucose in the form of a mixed solution and enzyme glucose oxidase. For the testing of the proposed circuit and the selection of a suitable material, the testing did not take place on measurements in whole blood. For the construction of the electrodes, the three most frequently used materials for the construction of electrodes used in clinical practice for sensing biopotentials, specifically the materials Ag/AgCl, Cu and Au, were used. The performed experiments showed that the material Ag/AgCl, which had the greatest sensitivity for the measurement even without the enzyme, was the most suitable material for the electrode. This conclusion is supported by the performed statistical analysis. On the basis of the testing, we can come to the conclusion that even if the Ag/AgCl electrode appears to be the most suitable, showing high stability, gold-plated electrodes showed stability throughout the measurement similarly to Ag/AgCl electrodes, but did not achieve the same qualities in sensitivity and readability of the measured results.
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Affiliation(s)
- Nikola Slaninova
- Department of Cybernetic and Biomedical Engineering, VŠB—Technical University of Ostrava, 17, listopadu 2172/15, 708 00 Ostrava–Poruba, Czech Republic; (N.S.); (K.F.); (K.D.)
| | - Klara Fiedorova
- Department of Cybernetic and Biomedical Engineering, VŠB—Technical University of Ostrava, 17, listopadu 2172/15, 708 00 Ostrava–Poruba, Czech Republic; (N.S.); (K.F.); (K.D.)
| | - Ali Selamat
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia;
- Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, Skudai 81310, Malaysia
| | - Karolina Danisova
- Department of Cybernetic and Biomedical Engineering, VŠB—Technical University of Ostrava, 17, listopadu 2172/15, 708 00 Ostrava–Poruba, Czech Republic; (N.S.); (K.F.); (K.D.)
| | - Jan Kubicek
- Department of Cybernetic and Biomedical Engineering, VŠB—Technical University of Ostrava, 17, listopadu 2172/15, 708 00 Ostrava–Poruba, Czech Republic; (N.S.); (K.F.); (K.D.)
| | - Ewaryst Tkacz
- Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, 40 Roosevelt’s Street, 41-800 Zabrze, Poland;
| | - Martin Augustynek
- Department of Cybernetic and Biomedical Engineering, VŠB—Technical University of Ostrava, 17, listopadu 2172/15, 708 00 Ostrava–Poruba, Czech Republic; (N.S.); (K.F.); (K.D.)
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