A Machine Learning Approach for Predicting Weight Gain Risks in Young Adults

Abstract

Individuals developing signs of weight gain or obesity are at a risk of developing serious illnesses such as type 2 diabetes, respiratory problems, coronary heart disease and stroke. Physical activity and healthy eating can be a fundamental component to maintain a healthy lifestyle. Therefore, detecting childhood obesity is of paramount importance. This paper utilises the vast amount of data available via the millennium cohort study. Various regression methods and artificial neural network models have been evaluated to predict the teenager BMI from earlier BMI values. The results obtained are encouraging and a prediction accuracy of over 90% has been achieved. Various issues relating to data mining and prediction accuracy are discussed.