Effective Analysis of Heart Disease Prediction using Machine Learning Techniques

Abstract

Heart disease is a type of chronic disease that affects the heart’s ability to operate. People are dying today as a result of the effects of heart disease. An effective strategy is employed to reduce the death rate of people. Machine learning (ML) is being used by researchers to detect heart disease quickly and accurately. Machine learning is one of the most effective ways of predicting the emergence of heart disease and assisting clinicians in their work. The prediction method aids in the identification of heart disease in its early phases. Machine learning technology is being used to predict and manage heart diseases using a variety of models. In this proposed study, machine learning (ML) techniques like Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), KNN, Support Vector Machine, and XGBoost will be used to detect heart disease throughout. By implementing these algorithms based on the electronic medical record, there is a better possibility of accurate heart disease identification and diagnosis. The system improves performance by identifying the most accurate model.