Prediction Of Health Status Using Machine Learning Techniques

In this era, everyone wants to be fit and healthy by maintaining proper diet plans. Fitness has become a major concern in the modern world. Body Mass Index (BMI) value has nowadays become the benchmark for deciding whether a person is obese or not, as obesity may lead to many health-related problems. Thus, BMI screens for weight categories i.e., obesity which may lead to health problems. This work aims to create an intelligent system which can give the accurate fitness level of a person based on his/her gender, height, and weight by calculating BMI. In this paper, Machine Learning models such as Logistic Regression, Random Forest (RF), K Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine and Gradient Boost algorithm are used to predict the status of obesity levels and have achieved accuracy of 97% by KNN, 96% by RF and 95% by DT. BMI index is used to predict the status of health of an individual. “BMI-prediction of health status” dataset from Kaggle is used for the implementation of the aforementioned models and achieved the highest accuracy of 97%.