A REVIEW ON OBESITY PREDICTION USING MACHINE LEARNING TECHNIQUES

Abstract

At present, protecting the community is crucial for addressing health issues, which can be done through medical research. Obesity has emerged as a global health crisis, posing a significant risk to the future. It ranks as one of the most prevalent health issues worldwide. Timely identification of a disease can assist both healthcare professionals and patients in taking action to reduce, if not completely eliminate, the underlying cause or in preventing the symptoms of the disease from worsening. Reviewing a patient's medical history is a common approach to diagnosing a disease; however, this process can be quite time-consuming when done manually and is often susceptible to errors and high costs. Thus, there is a compulsory to scientifically create a predictive model for the development of diseases using automated methods in today's world. This research highlights the capabilities of machine learning in tackling public health issues, laying the groundwork for future studies aimed at improving obesity prediction and prevention methods. Four machine learning algorithms were engaged: Random Forest, Decision Tree, K-Nearest Neighbor, and Support Vector Machine. The results have been encouraging, with the Random Forest classifier got the highest accuracy at 96.93% among all.