Predicting metabolic syndrome using risk quantification and ensemble methods

摘要

Metabolic syndrome which precedes the increased incidence of cardiovascular, disease and Type 2 diabetes and other noncommunicable diseases is defined as a group of metabolic dysfunction including glucose intolerance, central obesity, hypertriglyceridemia, dyslipidemia, and hypertension. This study aims to employ areal similarity degree risk quantification and MediBoost methods; to predict the risk of metabolic syndrome. This research is a practical one in which data from 11,237 participants of the CLUSTer Cohort Study in Malaysia has been utilized. Metabolic syndrome risk factors used are: fasting blood glucose, waist circumference, triglycerides, HDL-cholesterol, systolic and diastolic blood pressure. The data set was stratified by gender and age groups into young, middle-age and old. Data was labeled based on JIS criteria. The MediBoost algorithm outperformed the ASD risk quantification in all the performance metrics for all the subgroups of the CLUSTer dataset. For example, in the young male subgroup the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating curve (AUC) are 87.64 (g7.g6), 46.54 (100), g8.g6 (97.46), g2.5 (go.63), 87.04 (100), 72.75 (g6.03) for ASD(MediBoost) respectively. In a two-way sign-to-sign test, the performance metrics of MediBoost is significantly more efficient than ASD. The MediBoost algorithm also generates a simple interpretable decision tree which can be used to aid both individuals and medical practitioners in the timely and accurate diagnosis of the risk of metabolic syndrome.