MLHeartDis:Can Machine Learning Techniques Enable to Predict Heart Diseases?

Heart disease is contributing one of the leading reasons of death in the contemporary world. The three major danger signs for heart disease are smoking, high blood pressure and cholesterol, and 47% of all US citizens have at least one of these risk factors. In the field of clinical data analysis, predicting cardiovascular disease is a major difficulty. In this case, Machine learning (ML) can be important for taking decisions and predictions about heart disease based on personal key indicators (e.g., blood pressure, cholesterol level, smoking, diabetic status, obesity, stroke, alcohol drinking) of heart disease. In this paper, we proposed six machine learning models using survey data of over 400k US residents from the year 2020. The six machine learning models-Xgboost, Adaboost, Random Forest, Decision Tree, Logistic Regression, and Naive Bayes have been compared in detail. Through the prediction model for heart disease, we achieved an improved performance level with an accuracy level of 91.57% for the prediction of heart diseases with the logistic regression model.