Heart Disease Prediction System Using Random Forest Technique

Abstract

Abstract: Heart disease prediction and diagnosis have always been challenging tasks for medical experts. Hospitals and other medical facilities offer pricey procedures and treatments for cardiac diseases. As a result, being able to people all around the world can take the necessary actions to treat cardiac disease before it becomes severe if it is discovered in its early stages. The main causes of heart disease, a severe problem in recent years, are drinking alcohol, smoking cigarettes, and not exercising. A significant amount of data generated over time by the health care sector has allowed machine learning to offer efficient results in decision-making and prediction. We attempt to predict probable heart conditions in patients using machine learning approaches. In this project, we compare various classifiers, including decision trees, Naive Bayes, logistic regression, SVM, and random forests. We also suggest an ensemble classifier, which performs hybrid classification by combining the best features of both strong and weak classifiers and can handle large amounts of training and validation samples. We contrast already-in-use classifiers with others that have been proposed, such as Ada-boost and XGboost, which can offer higher accuracy. The main advantages of heart disease prediction using machine learning are that it manages the largest (enormous) quantity of data using the random forest algorithm and feature selection, as well as reducing the complexity of the doctors' time and being cost- and patient-friendly.