COMPARATIVE STUDY OF DETECTING OBESITY USING MACHINE LEARNING ALGORITHMS

Abstract

The terms "overweight" and "obesity" refer to weight increase that is unnatural or excessive and creates a health concern. Overweight is defined as a BMI of 25 or higher, and obesity as a BMI of 30 or higher. It is termed as a medical condition which increases the risk of medical conditions like heart diseases, hypertension, Type 2 diabetes, sleep apnea, coronary heart disease, etc. Dietary intake, inactivity, specific environmental variables, heredity, and medical conditions are the main causes of obesity. Adults in the current generation are more likely to be obese due to their unhealthy lifestyles and stress. In this paper we applied classification machine learning algorithms KNN and DT and compared those algorithms in obesity from the Data collected from UCI Machine Learning Repository, resulting in the nearest accurate outcomes. we try to implement a model which will help us get an accurate measure to predict the obesity level from the data.