A Machine Learning Approach for NonInvasive Diagnosis of Metabolic Syndrome 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 underdiagnosed 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 noninvasive 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 biomarkers, which significantly facilitates early detection.

Potential predictors of type2 diabetes risk: machine learning, synthetic data and wearable health devices Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients. In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statistical model able to recapitulate the simulated dynamics. The resulting machine learning model adequately predicts the synthetic data and can therefore be used as a computationallycheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self assessment by informed and aware individuals.