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Zhao R, Gu L, Ke X, Deng X, Li D, Ma Z, Wang Q, Zheng H, Yang Y. Risk prediction of cholangitis after stent implantation based on machine learning. Sci Rep 2024; 14:13715. [PMID: 38877118 PMCID: PMC11178872 DOI: 10.1038/s41598-024-64734-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/12/2024] [Indexed: 06/16/2024] Open
Abstract
The risk of cholangitis after ERCP implantation in malignant obstructive jaundice patients remains unknown. To develop models based on artificial intelligence methods to predict cholangitis risk more accurately, according to patients after stent implantation in patients' MOJ clinical data. This retrospective study included 218 patients with MOJ undergoing ERCP surgery. A total of 27 clinical variables were collected as input variables. Seven models (including univariate analysis and six machine learning models) were trained and tested for classified prediction. The model' performance was measured by AUROC. The RFT model demonstrated excellent performances with accuracies up to 0.86 and AUROC up to 0.87. Feature selection in RF and SHAP was similar, and the choice of the best variable subset produced a high performance with an AUROC up to 0.89. We have developed a hybrid machine learning model with better predictive performance than traditional LR prediction models, as well as other machine learning models for cholangitis based on simple clinical data. The model can assist doctors in clinical diagnosis, adopt reasonable treatment plans, and improve the survival rate of patients.
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Affiliation(s)
- Rui Zhao
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Lin Gu
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Xiquan Ke
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Xiaojing Deng
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Dapeng Li
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Zhenzeng Ma
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Qizhi Wang
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Hailun Zheng
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China.
| | - Yong Yang
- School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, China.
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Yang J, Jiang B, Qiu Z, Meng Y, Zhang X, Yu S, Dai F, Qian Y. Prediction of rhinitis with class imbalance based on heterogeneous ensemble learning. Comput Methods Biomech Biomed Engin 2024:1-16. [PMID: 38602489 DOI: 10.1080/10255842.2024.2339461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 04/01/2024] [Indexed: 04/12/2024]
Abstract
Common clinical rhinitis is characterized by different types of cases and class imbalance. Its prediction belongs to multiple output classification. Low recognition rate and poor generalization performance often occur for minority class. Therefore, we propose a novel integrated classification model, ARF-OOBEE, which transforms the multi-output classification to multi-label classification and multi-class classification. The multi-label classifier automatically adjusts the number and depth of integrated forest learners according to the imbalance ratio of single class label in a subset. It can effectively reduce the impact of class imbalance on classification and improve prediction performance of both majority or minority class concurrently. Also, we build a multi-class classification based on out-of-bag Extra-Tree to accomplish finer classification for the predicted labels. In addition, we calculate the feature importance for rhinitis on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features. We conduct 12 folds cross-validation experiments on 461 cases of clinical rhinitis. The outcomes show that the evaluation indicators of ARF-OOBEE, such as Sensitivity, Specificity, Accuracy, F1-Score, AUC, and G-Mean are 74.9%,86.5%,92.0%,78.3%,95.3%, and 79.9%, respectively. In comparison to the other methods, ARF-OOBEE has better evaluation indicator and is more effective for the early clinical diagnosis of rhinitis.
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Affiliation(s)
- Jingdong Yang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Biao Jiang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Zehao Qiu
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yifei Meng
- School of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Xiaolin Zhang
- Department of Otorhinolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shaoqing Yu
- Department of Otorhinolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fu Dai
- Department of Otorhinolaryngology, Antin Hospital, Shanghai, China
| | - Yue Qian
- Department of Otorhinolaryngology, Antin Hospital, Shanghai, China
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Lin L, Liu Y, Gao M, Rezaeipanah A. Improving hepatocellular carcinoma diagnosis using an ensemble classification approach based on Harris Hawks Optimization. Heliyon 2024; 10:e23497. [PMID: 38169861 PMCID: PMC10758797 DOI: 10.1016/j.heliyon.2023.e23497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 09/20/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Hepato-Cellular Carcinoma (HCC) is the most common type of liver cancer that often occurs in people with chronic liver diseases such as cirrhosis. Although HCC is known as a fatal disease, early detection can lead to successful treatment and improve survival chances. In recent years, the development of computer recognition systems using machine learning approaches has been emphasized by researchers. The effective performance of these approaches for the diagnosis of HCC has been proven in a wide range of applications. With this motivation, this paper proposes a hybrid machine learning approach including effective feature selection and ensemble classification for HCC detection, which is developed based on the Harris Hawks Optimization (HHO) algorithm. The proposed ensemble classifier is based on the bagging technique and is configured based on the decision tree method. Meanwhile, HHO as an emerging meta-heuristic algorithm can select a subset of the most suitable features related to HCC for classification. In addition, the proposed method is equipped with several strategies for handling missing values and data normalization. The simulations are based on the HCC dataset collected by the Coimbra Hospital and University Center (CHUC). The results of the experiments prove the acceptable performance of the proposed method. Specifically, the proposed method with an accuracy of 97.13 % is superior in comparison with the equivalent methods such as LASSO and DTPSO.
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Affiliation(s)
- LiuRen Lin
- Department of Pharmacy and Machinery, Qujing Second People's Hospital, Yunnan, Qujing, 655000, China
| | - YunKuan Liu
- Yunnan University of Chinese Medicine, Yunnan Key Laboratory of External Drug Delivery System and Preparation Technology in Universities, Yunnan, Kunming, 650500, China
| | - Min Gao
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Yunnan, Kunming, 650500, China
| | - Amin Rezaeipanah
- Department of Computer Engineering, Persian Gulf University, Bushehr, Iran
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4
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Yan W, Chiu B, Shen Z, Yang Q, Syer T, Min Z, Punwani S, Emberton M, Atkinson D, Barratt DC, Hu Y. Combiner and HyperCombiner networks: Rules to combine multimodality MR images for prostate cancer localisation. Med Image Anal 2024; 91:103030. [PMID: 37995627 DOI: 10.1016/j.media.2023.103030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 09/22/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023]
Abstract
One of the distinct characteristics of radiologists reading multiparametric prostate MR scans, using reporting systems like PI-RADS v2.1, is to score individual types of MR modalities, including T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels. First, we demonstrate that either a linear mixture model or a nonlinear stacking model is sufficient to model PI-RADS decision rules for localising prostate cancer. Second, parameters of these combining models are proposed as hyperparameters, weighing independent representations of individual image modalities in the Combiner network training, as opposed to end-to-end modality ensemble. A HyperCombiner network is developed to train a single image segmentation network that can be conditioned on these hyperparameters during inference for much-improved efficiency. Experimental results based on 751 cases from 651 patients compare the proposed rule-modelling approaches with other commonly-adopted end-to-end networks, in this downstream application of automating radiologist labelling on multiparametric MR. By acquiring and interpreting the modality combining rules, specifically the linear-weights or odds ratios associated with individual image modalities, three clinical applications are quantitatively presented and contextualised in the prostate cancer segmentation application, including modality availability assessment, importance quantification and rule discovery.
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Affiliation(s)
- Wen Yan
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Hong Kong China; Centre for Medical Image Computing; Department of Medical Physics & Biomedical Engineering; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Gower St, WC1E 6BT, London, UK.
| | - Bernard Chiu
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Hong Kong China; Department of Physics & Computer Science, Wilfrid Laurier University, 75 University Avenue West Waterloo, Ontario N2L 3C5, Canada.
| | - Ziyi Shen
- Centre for Medical Image Computing; Department of Medical Physics & Biomedical Engineering; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Gower St, WC1E 6BT, London, UK.
| | - Qianye Yang
- Centre for Medical Image Computing; Department of Medical Physics & Biomedical Engineering; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Gower St, WC1E 6BT, London, UK.
| | - Tom Syer
- Centre for Medical Imaging, Division of Medicine, University College London, London W1 W 7TS, UK.
| | - Zhe Min
- Centre for Medical Image Computing; Department of Medical Physics & Biomedical Engineering; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Gower St, WC1E 6BT, London, UK.
| | - Shonit Punwani
- Centre for Medical Imaging, Division of Medicine, University College London, London W1 W 7TS, UK.
| | - Mark Emberton
- Division of Surgery & Interventional Science, University College London, Gower St, WC1E 6BT, London, UK.
| | - David Atkinson
- Centre for Medical Imaging, Division of Medicine, University College London, London W1 W 7TS, UK.
| | - Dean C Barratt
- Centre for Medical Image Computing; Department of Medical Physics & Biomedical Engineering; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Gower St, WC1E 6BT, London, UK.
| | - Yipeng Hu
- Centre for Medical Image Computing; Department of Medical Physics & Biomedical Engineering; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Gower St, WC1E 6BT, London, UK.
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5
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Abdullah AD, Amanpour-Gharaei B, Nassiri Toosi M, Delazar S, Saligheh Rad H, Arian A. Comparing Texture Analysis of Apparent Diffusion Coefficient MRI in Hepatocellular Adenoma and Hepatocellular Carcinoma. Cureus 2024; 16:e51443. [PMID: 38298321 PMCID: PMC10829059 DOI: 10.7759/cureus.51443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 11/19/2023] [Indexed: 02/02/2024] Open
Abstract
AIM This study aimed to assess the effectiveness of using MRI-apparent diffusion coefficient (ADC) map-driven radiomics to differentiate between hepatocellular adenoma (HCA) and hepatocellular carcinoma (HCC) features. MATERIALS AND METHODS The study involved 55 patients with liver tumors (20 with HCA and 35 with HCC), featuring 106 lesions equally distributed between hepatic carcinoma and hepatic adenoma who underwent texture analysis on ADC map MR images. The analysis identified several imaging features that significantly differed between the HCA and HCC groups. Four classification models were compared for distinguishing HCA from HCC including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), random forest (RF), and k-nearest neighbor (KNN). RESULTS The k-nearest neighbor (KNN) classifier displayed the top accuracy (0.89) and specificity (0.90). Linear-SVM and KNN classifiers showcased the leading sensitivity (0.88) for both, with the KNN classifier achieving the highest precision (0.9). In comparison, the conventional interpretation had lower sensitivity (70.1%) and specificity (77.9%). CONCLUSION The study found that utilizing ADC maps for texture analysis in MR images is a viable method to differentiate HCA from HCC, yielding promising results in identified texture features.
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Affiliation(s)
- Ayoob Dinar Abdullah
- Technology of Radiology and Radiotherapy, Tehran University of Medical Sciences, Tehran, IRN
| | - Behzad Amanpour-Gharaei
- Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, IRN
| | | | - Sina Delazar
- Advanced Diagnostic and Interventional Radiology Research Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, IRN
| | - Hamidraza Saligheh Rad
- Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, IRN
| | - Arvin Arian
- Radiology, Cancer Institute, Tehran University of Medical Sciences, Tehran, IRN
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Maher A, Mian Qaisar S, Salankar N, Jiang F, Tadeusiewicz R, Pławiak P, Abd El-Latif AA, Hammad M. Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning. Biocybern Biomed Eng 2023; 43:463-475. [DOI: 10.1016/j.bbe.2023.05.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
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Othman E, Mahmoud M, Dhahri H, Abdulkader H, Mahmood A, Ibrahim M. Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models. SENSORS (BASEL, SWITZERLAND) 2022; 22:5429. [PMID: 35891111 PMCID: PMC9322134 DOI: 10.3390/s22145429] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/02/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.
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Affiliation(s)
- Esam Othman
- Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia; (E.O.); (H.D.); (A.M.)
| | - Muhammad Mahmoud
- Department of Information Systems, Madina Higher Institute of Management and Technology, Shabramant 12947, Egypt;
| | - Habib Dhahri
- Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia; (E.O.); (H.D.); (A.M.)
| | - Hatem Abdulkader
- Department of Information Systems, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, Egypt;
| | - Awais Mahmood
- Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia; (E.O.); (H.D.); (A.M.)
| | - Mina Ibrahim
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, Egypt
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Hammad M, Bakrey M, Bakhiet A, Tadeusiewicz R, El-Latif AAA, Pławiak P. A novel end-to-end deep learning approach for cancer detection based on microscopic medical images. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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9
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A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136427] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.
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Książek W, Turza F, Pławiak P. NCA-GA-SVM: A new two-level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3599. [PMID: 35403827 DOI: 10.1002/cnm.3599] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 03/25/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
Hepatocellular carcinoma (HCC) is one of the major challenges facing biomedical research. Despite the high lethality, methods to predict mortality for this type of aggressive malignant tumor are insufficient. Machine learning is recognized by many authors as a valuable, yet poorly studied tool in this field. Undoubtedly, searching for new feature selection methods is significant in building an effective machine-learning model. In this study, we propose the novel hybrid model using neighborhood components analysis, genetic algorithm and support vector machine classifier (NCA-GA-SVM). Because SVM works with default parameters characterized by low classification results, we decided to use GA for the proper optimization and feature selection. As reported in the available literature, NCA and GA obtain high classification results. Here, we decided to combine these approaches, building a two-level algorithm for HCC fatality prognosis. We used a well-known dataset collected from 165 patients at Coimbra's Hospital and University Center, Portugal. Our results revealed 96.36% classification accuracy and 95.52% F1-score. Additionally, we compared all data for these metrics published so far. We demonstrated that our algorithm achieved the highest accuracy and can be successfully applied for the assessment of hepatocellular carcinoma mortality in the future. Our findings bring methodological value for future HCC studies and emphasize the possibility of using machine-learning techniques to improve the quality of medical decisions.
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Affiliation(s)
- Wojciech Książek
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Kraków, Poland
| | - Filip Turza
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, Poland
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Kraków, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland
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11
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Sakr AS, Pławiak P, Tadeusiewicz R, Hammad M. Cancelable ECG biometric based on combination of deep transfer learning with DNA and amino acid approaches for human authentication. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.11.066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:6342226. [PMID: 34992648 PMCID: PMC8727098 DOI: 10.1155/2021/6342226] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/27/2021] [Indexed: 12/31/2022]
Abstract
Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients' survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
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Gouda G, Gupta MK, Donde R, Behera L, Vadde R. Metabolic pathway-based target therapy to hepatocellular carcinoma: a computational approach. THERANOSTICS AND PRECISION MEDICINE FOR THE MANAGEMENT OF HEPATOCELLULAR CARCINOMA, VOLUME 2 2022:83-103. [DOI: 10.1016/b978-0-323-98807-0.00003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
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14
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A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:4243700. [PMID: 34567101 PMCID: PMC8463188 DOI: 10.1155/2021/4243700] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/09/2021] [Accepted: 09/06/2021] [Indexed: 12/12/2022]
Abstract
The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system's performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system's improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.
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Hammad M, Kandala RN, Abdelatey A, Abdar M, Zomorodi‐Moghadam M, Tan RS, Acharya UR, Pławiak J, Tadeusiewicz R, Makarenkov V, Sarrafzadegan N, Khosravi A, Nahavandi S, EL-Latif AAA, Pławiak P. Automated detection of shockable ECG signals: A review. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.05.035] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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16
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Transmission Quality Classification with Use of Fusion of Neural Network and Genetic Algorithm in Pay&Require Multi-Agent Managed Network. SENSORS 2021; 21:s21124090. [PMID: 34198587 PMCID: PMC8231990 DOI: 10.3390/s21124090] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/14/2021] [Accepted: 06/11/2021] [Indexed: 11/21/2022]
Abstract
Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through the network. The data transmission quality can be described using parameters, i.e., delay, bandwidth, packet loss ratio and jitter. On the basis of the obtained values, specialists are able to state how measured parameters impact on the overall quality of the provided service. Unfortunately, for a non-expert user, understanding of these parameters can be too complex. Hence, the problem of translation of the parameters describing the transmission quality appears understandable to the user. This article presents the concept of using Machine Learning (ML) to solve the above-mentioned problem, i.e., a dynamic classification of the measured parameters describing the transmission quality in a certain scale. Thanks to this approach, describing the quality will become less complex and more understandable for the user. To date, some studies have been conducted. Therefore, it was decided to use different approaches, i.e., fusion of a neural network (NN) and a genetic algorithm (GA). GA’s were choosen for the selection of weights replacing the classic gradient descent algorithm. For learning purposes, 100 samples were obtained, each of which was described by four features and the label, which describes the quality. In the reasearch carried out so far, single classifiers and ensemble learning have been used. The current result compared to the previous ones is better. A relatively high quality of the classification was obtained when we have used 10-fold stratified cross-validation, i.e., SEN = 95% (overall accuracy). The incorrect classification was 5/100, which is a better result compared to previous studies.
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Książek W, Gandor M, Pławiak P. Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma. Comput Biol Med 2021; 134:104431. [PMID: 34015670 DOI: 10.1016/j.compbiomed.2021.104431] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/14/2021] [Accepted: 04/21/2021] [Indexed: 11/18/2022]
Abstract
Hepatocellular carcinoma (HCC) is the most common liver cancer in adults. Many different factors make it difficult to diagnose in humans.. In this paper, a novel diagnostics approach based on machine learning techniques is presented. Logistic regression is one of the most classic machine learning models used to solve the problem of binary classification. In typical implementations, logistic regression coefficients are optimized using iterative methods. Additionally, parameters such as solver, C - a regularization parameter or the number of iterations of the algorithm operation should be selected. In our research, we propose a combination of logistic regression with genetic algorithms. We present three experiments showing the fusion of those methods. In the first experiment, we genetically select the logistic regression parameters, while the second experiment extends this approach by including a genetic selection of features. The third experiment presents a novel approach to train the logistic regression model - the genetic selection of coefficients (weights). Our models are tested for the survival prediction of hepatocellular carcinoma based on patient data collected at Coimbra's Hospital and Universitary Center (CHUC), Portugal. The model we proposed achieved a classification accuracy of 94.55% and an f1-score of 93.56%. Our algorithm shows that machine learning techniques optimized by the proposed concept can bring a new and accurate approach in HCC diagnosis with high accuracy.
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Affiliation(s)
- Wojciech Książek
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland
| | - Michał Gandor
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland.
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18
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Chandran V, Sumithra MG, Karthick A, George T, Deivakani M, Elakkiya B, Subramaniam U, Manoharan S. Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5584004. [PMID: 33997017 PMCID: PMC8112909 DOI: 10.1155/2021/5584004] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 12/17/2022]
Abstract
Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.
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Affiliation(s)
- Venkatesan Chandran
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - M. G. Sumithra
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - Alagar Karthick
- Renewable Energy Lab, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - Tony George
- Department of Electrical and Electronics Engineering, Adi Shankara Institute of Engineering and Technology Mattoor, Kalady, Kerala 683574, India
| | - M. Deivakani
- Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, 624622 Tamilnadu, India
| | - Balan Elakkiya
- Department of Electronics and Communication Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Tamilnadu 600062, India
| | - Umashankar Subramaniam
- Department of Communications and Networks, Renewable Energy Lab, College of Engineering, Prince, Sultan University, Riyadh 12435, Saudi Arabia
| | - S. Manoharan
- Department of Computer Science, School of Informatics and Electrical Engineering, Institute of Technology, Ambo University, Ambo, Post Box No. 19, Ethiopia
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