Cardiovascular disease prediction model using Machine Learning Algorithm

Abstract

This research uses machine learning to anticipate and detect the symptoms of specific diseases after examining some of the important elements of these diseases in order to better understand them and develop new and better treatment techniques. This study uses machine learning and generated data sets to evaluate and categorize the signs and symptoms of heart diseases. We’d like to see whether we can improve individual disease prediction processes so that we can predict cardiovascular diseases and their modalities more accurately. Therefore, the aim of this study is also to develop a more diversified model from the existing ones. We are focusing on cardiovascular diseases, which is among the world’s top causes of death. Multiple machine learning (ML) algorithms are being used more frequently to predict cardiovascular disease. We want to evaluate and describe how well ML algorithms generally forecast cardiovascular illnesses. This research analyzes the classification of cardiovascular disease using machine learning methods including Random Forest (RF), Logistic Regression, Decision Tree, Na¨ıve Bayes, Linear Algorithm, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Neural Network. We anticipate finding effective and efficient results that will aid in better diagnosing these cardiovascular diseases and also will help us for developing better treatment procedures.