A Review on Cardiovascular Disease/Heart Disease by Machine Learning Prediction

Cardiovascular diseases commonly referred to as heart diseases, constitute a wide-ranging group of ailments impacting the heart. In our rapidly advancing technological era, machine learning plays a pivotal role in disease prediction. Among the plethora of health issues, heart and cardiovascular diseases stand out with elevated mortality rates. Machine learning techniques prove instrumental in anticipating these conditions, offering clinicians invaluable insights for early diagnosis and treatment. This review explores the application of various machine learning algorithms such as Naive Bayes, Random Forest, Logistic Regression, K-Nearest Neighbors, and Decision Trees in forecasting cardiac diseases. These algorithms not only predict but also categorize individuals with heart diseases, enhancing the accuracy of early detection. The integration of machine learning into healthcare demonstrates its potential to revolutionize predictive medicine, providing a proactive approach to managing cardiovascular illnesses. The proactive identification of cardiac diseases through these techniques empowers healthcare professionals with timely information, ultimately improving patient outcomes.