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Ahmed W, Shahid B, Aziz N, Afzal F, Ur Rehman A, Zafar F. Automatic Diagnosis of Cataract and Myopia Through Fundus Images. 2023 INTERNATIONAL CONFERENCE ON BUSINESS ANALYTICS FOR TECHNOLOGY AND SECURITY (ICBATS) 2023. [DOI: 10.1109/icbats57792.2023.10111388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Wajeeha Ahmed
- Riphah International University,Department of Computer Science,Lahore,Pakistan
| | - Bisma Shahid
- Riphah International University,Department of Computer Science,Lahore,Pakistan
| | - Nauman Aziz
- NCBA&E,School of Computer Science,Lahore,Pakistan
| | | | - Abd Ur Rehman
- Riphah International University,Department of Computer Science,Lahore,Pakistan
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Stephen M, Felix A. Fuzzy AHP point factored inference system for detection of cardiovascular disease. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The World health organization (WHO) reported that cardiovascular disease is the leading cause of death worldwide, particularly in developing countries. But while diagnosing cardiovascular disease, medical practitioners might have differences of opinions and faced challenging when there is inadequate information and uncertainty of the problem. Therefore, to resolve ambiguity and vagueness in diagnosing disease, a perfect decision-making model is required to assist medical practitioners in detecting the disease at an early stage. Thus, this study designs a fuzzy analytic hierarchy process (FAHP) point-factored inference system to detect cardiovascular disease. The attributes are selected and classified into sub-attributes and point factor scale using the clinical data, medical practitioners, and literature review. Fuzzy AHP is used in calculating the attribute weights, the strings are generated using the Mamdani fuzzy inference system, and the strength of each set of fuzzy rules is calculated by multiplying the attribute weights with the point factor scale. The string weights determine the output ranges of cardiovascular disease. Moreover, the results are validated using sensitivity analysis, and comparative analysis is performed with AHP techniques. The results show that the proposed method outperforms other methods, which are elucidated by the case study.
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Affiliation(s)
- M. Stephen
- Mathematics Division, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai, TamilNadu, India
| | - A. Felix
- Mathematics Division, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai, TamilNadu, India
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Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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Liu W, Liu X, Peng M, Chen GQ, Liu PH, Cui XW, Jiang F, Dietrich CF. Artificial intelligence for hepatitis evaluation. World J Gastroenterol 2021; 27:5715-5726. [PMID: 34629796 PMCID: PMC8473592 DOI: 10.3748/wjg.v27.i34.5715] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/28/2021] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
Recently, increasing attention has been paid to the application of artificial intelligence (AI) to the diagnosis of diverse hepatic diseases, which comprises traditional machine learning and deep learning. Recent studies have shown the possible value of AI based data mining in predicting the incidence of hepatitis, classifying the different stages of hepatitis, diagnosing or screening for hepatitis, forecasting the progression of hepatitis, and predicting response to antiviral drugs in chronic hepatitis C patients. More importantly, AI based on radiology has been proven to be useful in predicting hepatitis and liver fibrosis as well as grading hepatocellular carcinoma (HCC) and differentiating it from benign liver tumors. It can predict the risk of vascular invasion of HCC, the risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis, and the risk of liver failure after hepatectomy in HCC patients. In this review, we summarize the application of AI in hepatitis, and identify the challenges and future perspectives.
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Affiliation(s)
- Wei Liu
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Xue Liu
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Mei Peng
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi 445000, Hubei Province, China
| | - Peng-Hua Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Shaoyang University, Shaoyang 422000, Hunan Province, China
| | - Xin-Wu Cui
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3626, Switzerland
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Oladimeji OO, Oladimeji A, Oladimeji O. Classification models for likelihood prediction of diabetes at early stage using feature selection. APPLIED COMPUTING AND INFORMATICS 2021. [DOI: 10.1108/aci-01-2021-0022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeDiabetes is one of the life-threatening chronic diseases, which is already affecting 422m people globally based on (World Health Organization) WHO report as at 2018. This costs individuals, government and groups a whole lot; right from its diagnosis stage to the treatment stage. The reason for this cost, among others, is that it is a long-term treatment disease. This disease is likely to continue to affect more people because of its long asymptotic phase, which makes its early detection not feasible.Design/methodology/approachIn this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas.FindingsThe study result shows that feature selection helps in getting better model, as it prevents overfitting and removes redundant data. Hence, the study result when compared with previous research shows the better result has been achieved, after it was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at diagnosing diabetes disease at its early stage.Originality/valueThis study has not been published anywhere else.
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Lee M, Tran DT, Lee JH. 3D Facial Pain Expression for a Care Training Assistant Robot in an Elderly Care Education Environment. Front Robot AI 2021; 8:632015. [PMID: 33996925 PMCID: PMC8118714 DOI: 10.3389/frobt.2021.632015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 02/04/2021] [Indexed: 11/13/2022] Open
Abstract
As the elderly population increases, the importance of the caregiver’s role in the quality of life of the elderly has increased. To achieve effective feedback in terms of care and nursing education, it is important to design a robot that can express emotions or feel pain like an actual human through visual-based feedback. This study proposes a care training assistant robot (CaTARo) system with 3D facial pain expression that simulates an elderly person for improving the skills of workers in elderly care. First, in order to develop an accurate and efficient system for elderly care training, this study introduces a fuzzy logic–based care training evaluation method that can calculate the pain level of a robot for giving the feedback. Elderly caregivers and trainees performed the range of motion exercise using the proposed CaTARo. We obtained quantitative data from CaTARo, and the pain level was calculated by combining four key parameters using the fuzzy logic method. Second, we developed a 3D facial avatar for use in CaTARo that is capable of expressing pain based on the UNBC-McMaster Pain Shoulder Archive, and we then generated four pain groups with respect to the pain level. To mimic the conditions for care training with actual humans, we designed the system to provide pain feedback based on the opinions of experts. The pain feedback was expressed in real time by using a projector and a 3D facial mask during care training. The results of the study confirmed the feasibility of utilizing a care training robot with pain expression for elderly care training, and it is concluded that the proposed approach may be used to improve caregiving and nursing skills upon further research.
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Affiliation(s)
- Miran Lee
- Advanced Intelligent System Laboratory, Graduate School of Information Science and Engineering, Ritsumeikan University, Shiga, Japan
| | - Dinh Tuan Tran
- Advanced Intelligent System Laboratory, Faculty of Information Science and Engineering, Ritsumeikan University, Shiga, Japan
| | - Joo-Ho Lee
- Advanced Intelligent System Laboratory, Faculty of Information Science and Engineering, Ritsumeikan University, Shiga, Japan
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Improved adaptive neuro-fuzzy inference system based on modified glowworm swarm and differential evolution optimization algorithm for medical diagnosis. Neural Comput Appl 2020; 33:7649-7660. [PMID: 33250576 PMCID: PMC7684855 DOI: 10.1007/s00521-020-05507-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 11/04/2020] [Indexed: 11/04/2022]
Abstract
Medical diagnosis has seen a tremendous advancement in the recent years due to the advent of modern and hybrid techniques that aid in screening and management of the disease. This paper figures a predictive model for detecting neurodegenerative diseases like glaucoma, Parkinson’s disease and carcinogenic diseases like breast cancer. The proposed approach focuses on enhancing the efficiency of adaptive neuro-fuzzy inference system (ANFIS) using a modified glowworm swarm optimization algorithm (M-GSO). This algorithm is a global optimization wrapper approach that simulates the collective behavior of glowworms in nature during food search. However, it still suffers from being trapped in local minima. Hence in order to improve glowworm swarm optimization algorithm, differential evolution (DE) algorithm is utilized to enhance the behavior of glowworms. The proposed (DE–GSO–ANFIS) approach estimates suitable prediction parameters of ANFIS by employing DE–GSO algorithm. The outcomes of the proposed model are compared with traditional ANFIS model, genetic algorithm-ANFIS (GA-ANFIS), particle swarm optimization-ANFIS (PSO-ANFIS), lion optimization algorithm-ANFIS (LOA-ANFIS), differential evolution-ANFIS (DE-ANFIS) and glowworm swarm optimization (GSO). Experimental results depict better performance and superiority of the DE–GSO–ANFIS over the similar methods in predicting medical disorders.
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M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing. MATHEMATICS 2020. [DOI: 10.3390/math8050707] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.
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Khan WA, Abbas S, Khan MA, Qazi WM, Khan MS. Intelligent task planner for cloud robotics using level of attention empowered with fuzzy system. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-2312-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Joloudari JH, Hassannataj Joloudari E, Saadatfar H, Ghasemigol M, Razavi SM, Mosavi A, Nabipour N, Shamshirband S, Nadai L. Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17030731. [PMID: 31979257 PMCID: PMC7037941 DOI: 10.3390/ijerph17030731] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/15/2020] [Accepted: 01/20/2020] [Indexed: 12/14/2022]
Abstract
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
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Affiliation(s)
- Javad Hassannataj Joloudari
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran; (J.H.J.); (H.S.); (M.G.)
| | - Edris Hassannataj Joloudari
- Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran;
| | - Hamid Saadatfar
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran; (J.H.J.); (H.S.); (M.G.)
| | - Mohammad Ghasemigol
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran; (J.H.J.); (H.S.); (M.G.)
| | - Seyyed Mohammad Razavi
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand 9717434765, Iran;
| | - Amir Mosavi
- Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary; (A.M.); (L.N.)
- Institute of Structural Mechanics, Bauhaus Universität-Weimar, D-99423 Weimar, Germany
- Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia
- Faculty of Health, Queensland University of Technology, 130 Victoria Park Road, Queensland 4059, Australia
| | - Narjes Nabipour
- Department Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
- Correspondence: (N.N.); (S.S.)
| | - Shahaboddin Shamshirband
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Correspondence: (N.N.); (S.S.)
| | - Laszlo Nadai
- Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary; (A.M.); (L.N.)
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