Obesity Prediction Using Ensemble Machine Learning Approaches

Abstract

At the present time, obesity is a serious health problem which causes many diseases such as diabetes, cancer, and heart ailments. Obesity, in turn, is caused by the accumulation of excess fat. There are many determinants of obesity, namely age, weight, height, and body mass index. The value of obesity can be computed in numerous ways; however, they are not generic enough to be applied in every context (such as a pregnant lady or an old man) and yet provide accurate results. To this end, we employ the R ensemble prediction model and Python interface. It is observed that on an average, the predicted values of obesity are 89.68% accurate. The ensemble machine learning prediction approach leverages generalized linear model, random forest, and partial least squares. The current work can further be improvised to predict other health parameters and recommend corrective measures based on obesity values.