Abstract
Metabolic syndrome is a disorder that affects the overall function of the human body. It is manifested by elevated levels of cholesterol and triglycerides, a significant reduction in energy levels, weight gain with visceral fat deposition in the abdomen, and menstrual disorders while increasing the risk of cardiovascular disease, autoimmune diseases and diabetes. A public dataset is exploited to evaluate the metabolic syndrome (MetS) occurrence risk in the elderly using Machine Learning (ML) techniques concerning Accuracy, Recall and Area Under Curve (AUC). The stacking method achieved the best performance. Finally, our purpose is to identify subjects at risk and promote earlier intervention to avoid the future development of MetS.
This work has been supported by the European Union’s H2020 research and innovation programme GATEKEEPER under grant agreement No 857223, SC1-FA-DTS-2018-2020 Smart living homes-whole interventions demonstrator for people at health and social risks.
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Dritsas, E., Alexiou, S., Moustakas, K. (2022). Metabolic Syndrome Risk Forecasting on Elderly with ML Techniques. In: Simos, D.E., Rasskazova, V.A., Archetti, F., Kotsireas, I.S., Pardalos, P.M. (eds) Learning and Intelligent Optimization. LION 2022. Lecture Notes in Computer Science, vol 13621. Springer, Cham. https://doi.org/10.1007/978-3-031-24866-5_33
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