User Input Based Health Risk Assessment to Predict Diabetes, Obesity and Heart Risk factors

Due to a shift in lifestyle, many young people are currently suffering from heart attacks, diabetes, and obesity. Because of work schedules, the sickness rate has grown since the covid epidemic. To stop this issue, early discovery is necessary. As a result, the constructed model will forecast the risk of obesity, diabetes, and heart attacks based on user input from their respective location. An individual's health risk assessment is very important in determining any health conditions that require urgent care. This makes it possible for someone to recognize potential health dangers and take the appropriate safety precautions. Thus, the goal of this proposed system is to develop a user-friendly Artificial Intelligence driven health risk assessment application that would give users a platform to do basic health risk assessments. Based on the user input, Machine Learning based health predictors generate the health analysis report and alert the user with proper guidance. K-Nearest Neighbor (KNN), Logistic Regression, Stochastic Gradient Boosting, Voting Ensemble and Decision Tree algorithms are used for accurate prediction of disease. The proposed application is cost effective since it uses a couple of features.