Paplomatas, P.; Rigas, D.; Sergounioti, A.; Vrahatis, A. Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning. Eng2024, 5, 1422-1434.
Paplomatas, P.; Rigas, D.; Sergounioti, A.; Vrahatis, A. Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning. Eng 2024, 5, 1422-1434.
Paplomatas, P.; Rigas, D.; Sergounioti, A.; Vrahatis, A. Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning. Eng2024, 5, 1422-1434.
Paplomatas, P.; Rigas, D.; Sergounioti, A.; Vrahatis, A. Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning. Eng 2024, 5, 1422-1434.
Abstract
The increasing prevalence of Metabolic Syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of Metabolic Syndrome, thereby addressing a significant concern in public health.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
