Prediction of metabolic ageing in higher education staff using machine learning: A pilot study

Prediction of metabolic ageing in higher education staff using machine learning: A pilot study

The detection of individuals with obesity or overweight allows to predict the prevalence of health risks, such as premature death, disabilities and other chronic diseases. This study describes a pilot conducted on the members of a higher education staff in the city of Matehuala, Mexico. It involved processing anthropometric measurements, health indicators and the results of bioelectrical impedance analysis using machine learning techniques. The goal was to identify the metabolic aging of individuals. The recorded data were used to create a database that was subsequently employed in four different classification models: decision tree, random forest, artificial neural networks and adaptive boosting. Additionally, four statistical techniques were utilized to determine variable importance scores: Pearson, Chi2, Anova, recursive elimination method and the variance inflation factor. The variable importance score was employed to identify the features that were most consistently repeated across methods. This analysis concluded that both anthropometric measurements and the results of bioelectrical impedance analysis provide valuable references for identifying obesity and overweight in individuals. Among the anthropometric measurements that exhibited a greater impact on the models' predictions were waist-to-height ratio, hip and arm circumferences, body mass index, systolic and diastolic blood pressure and heart rate. Additionally, body fat and muscle mass also contributed significantly.