Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements

BackgroundThe aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches. MethodsA prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35-65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist-to-height Ratio (WHtR), and Waist-to-hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate the DT's accuracy, sensitivity, and specificity using R software. ResultsTraditionally, 1461 women and 875 men with T2DM (T2DM group). According to the LR, in males, WC and BIA (p-value < 0.001) and in females, demispan and WC (p-value < 0.001) had the highest correlation with T2DM development risk. The DT indicated that WC has the most crucial effect on T2DM development risk, followed by HC, and BAI. ConclusionsOur results showed that in both men and women, WC was the most important anthropometric factor to predict T2DM.

Automated summary

This study used machine learning approaches to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM). The results showed that waist circumference (WC) was the most important predictor for T2DM.