Experimental study of a medical data analysis model based on comparative performance of classification algorithms
Ismukhamedova A. Uvaliyeva I. Rakhmetullina Z.
October 2024Institute of Advanced Engineering and Science
Indonesian Journal of Electrical Engineering and Computer Science
2024#36Issue 1672 - 684 pp.
This article centers around the development and analysis of machine learning (ML) and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of ML and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain.
Diabetes prevalence , Digital healthcare , Electronic health system , Health informatics , Healthcare resource allocation , Intelligent decision support , Medical data research , Support of medical decisions
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School of Computer Science, D. Serikbayev East Kazakhstan Technical University, Oskemen, Kazakhstan
School of Digital Technology and Intelligent Systems, D. Serikbayev East Kazakhstan Technical University, Oskemen, Kazakhstan
School of Computer Science
School of Digital Technology and Intelligent Systems
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