Discrimination of Feature Influence Model for Obesity Prediction using Machine Learning Techniques

It has been generally observed that a set of clinically established features can be used to predict obesity. Due to lifestyle habits, most of the population deviates from the suggested treatment to control the state of obesity. This study is an experimental analysis of the effect of related features on the classification of obesity. Two research questions have been designed: “With what degree of accuracy can obesity be categorized using a feature vector (FS) with 16 features?” (RQ1), and, “Can a feature subset (FSS) classify the disease with an accuracy of over 90% compared to the accuracy obtained in RQ1?” (RQ2). It was observed that an FS comprising 16 features reflected an accuracy of 96.68% in the classification of obesity in RQ1, and an FSS comprising four features (selected using the SelectKBest algorithm) exhibited an accuracy of 88.38% on the same dataset. Since 88.38% is 91.42% of 96.68%, the FSS attains accuracy over 90% concerning FS in classifying obesity. Three machine learning (ML) models were selected based on the best accuracy values in the literature. Moreover, both RQ1 and RQ2 have far better accuracy than other methods.