Timely Detection of Diabetes with Support Vector Machines, Neural Networks and Deep Neural Networks

426008f3-56e1-4234-b2c6-6207d649ed9e20230908085548190wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTER RESEARCH2415-15211991-875510.37394/232018http://wseas.org/wseas/cms.action?id=13372214202321420231110.37394/232018.2023.11https://wseas.com/journals/cr/2023.phpTimely Detection of Diabetes with Support Vector Machines, Neural Networks and Deep Neural NetworksRumenValchevDepartment Fundamentals of Electrical Engineering, Technical University of Sofia, Sofia, 8 St. Kliment Ohridski Blvd., BULGARIAMiroslavNikolovDepartment Fundamentals of Electrical Engineering, Technical University of Sofia, Sofia, 8 St. Kliment Ohridski Blvd., BULGARIAOgnyanNakovFaculty Computer Systems and Technologies, Technical University of Sofia, Sofia, 8 St. Kliment Ohridski Blvd., BULGARIAMilenaLazarovaFaculty Computer Systems and Technologies, Technical University of Sofia, Sofia, 8 St. Kliment Ohridski Blvd., BULGARIAValeriMladenovDepartment Fundamentals of Electrical Engineering, Technical University of Sofia, Sofia, 8 St. Kliment Ohridski Blvd., BULGARIAIn this paper, we describe an expert system with three tools - Support Vector Machine (SVM), Deep Neural Network (DNN), and feed-forward neural network (NN) in MATLAB and Python to identify potential candidates with diabetes at the initial stages of the disease. To achieve this goal, the importance of the main factors associated with previous health problems and the onset of diabetes in individuals with a medical history is analyzed. By recognizing the common early indications of diabetes, the system can aid in the selection of patients and potentially benefit them by detecting the disease at an early stage and applying appropriate and timely healing.97202397202326327424https://wseas.com/journals/cr/2023/a485118-011(2023).pdf10.37394/232018.2023.11.24https://wseas.com/journals/cr/2023/a485118-011(2023).pdf10.4324/9781315244839Bachenheimer, J., Brescia, B., Reinventing Patient Recruitment: Revolutionary Ideas for Clinical Trial Success, Gower Publishing. 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