A Machine Learning Approach for Non-Invasive Diagnosis of Metabolic Syndrome

Abstract

The metabolic syndrome is one of the major public health challenges worldwide. Prevalence of metabolic syndrome vastly increases the risk of type 2 diabetes and cardiovascular diseases (CVDs). Metabolic Syndrome in general is under-diagnosed and often goes undetected for years. In this paper we present a machine learning based method for early detection of metabolic syndrome which uses only non-invasive features. We train and test our model based on data collected from a German population consisting of 2314 subjects (male = 918, female = 1396). Out of 2314 subjects 941 were diagnosed with metabolic syndrome (male = 441, female = 500). Features we consider include different anthropometric features (such as height, weight, waist circumference), medications, age, gender etc.; machine learning techniques we employed included gradient boosting machines, random forest, logistic regression and an ensemble model. We compare our models against the ones that were proposed in previous literature and outperform them in our cohort. We achieve area under the curve values (AUCs) of up to 0.90 with the ensemble classifier. The results achieved suggest that machine learning can be a valuable tool to predict metabolic syndrome with high discriminative power without relying on any invasive bio-markers, which significantly facilitates early detection.