Association and Predictive Capability of Body Composition and Diabetes Mellitus Using Artificial Intelligence: A cohort study

Abstract

Abstract This study aimed to investigate the association between regional body fat distribution and the prevalence of Diabetes mellitus (DM) in adult populations using machine learning. We applied machine learning methods to data from a cohort study to analyze the relationship between fat in different body areas and diabetes. All measurement was done by "Tanita Segmental Body Composition Analyzer BC-418 MA Tanita Corp, Japan". The correlation between the used parameters and DM was measured using some machine learning algorithms i.e. SVM, SGD, KNN, MLP, Adaboost and EDINet. A total of 4661 participants were included. The top features that reported higher importance in classification models were age, fat mass, and percentages in legs, arms, and trunk area. Fat-free mass in the legs, arm, and trunk area was reversely associated with diabetes. Our proposed method significantly outperformed the others. It has the best performances in Accuracy, Precision, Recall-0, Recall-1, and F1-score, which were 93.57, 93.67, 96.11, 74.55 and 93.62, respectively. Our machine learning models showed that regional body fat could have specific impacts on diabetes based on the location of the fat accumulation. The most predictor values of diabetes were age, fat mass, and percentages in arms, legs, and trunk area. Further studies on different ages, gender, ethnic groups, and races are recommended.