Metabolic syndrome prediction using non-invasive and dietary parameters based on a support vector machine

Metabolic syndrome (MetS) is a widely used index for finding people at risk for chronic diseases, including cardiovascular disease and diabetes. Early detection of MetS is especially important in prevention programs. Relying on previous studies that suggest machine learning methods as a valuable approach for diagnosing MetS, this study aimed to develop MetS prediction models based on support vector machine (SVM) algorithms, applying non-invasive and low-cost (NI&LC), and also dietary parameters.

Source: Nutrition, Metabolism, and Cardiovascular Diseases : NMCD - September 5, 2023 Category: Nutrition Authors: Sahar Mohseni-Takalloo, Hassan Mozaffari-Khosravi, Hadis Mohseni, Masoud Mirzaei, Mahdieh Hosseinzadeh Source Type: research