Predictive Analytics for Heart Disease Using Machine Learning

Abstract

This innovative paper addresses one of the critical issues in medical data analysis is accurately predicting a patient’s risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. Various machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM), were trained on historical datasets to predict the probability of heart disease with high accuracy. The proposed system aims to assist clinicians by providing an automated, accurate, and non-invasive tool for early detection of heart disease. It also has the potential to raise public awareness, enabling individuals to take preventive actions based on their predicted risk factors. Future enhancements include the incorporation of more advanced algorithms, real-time monitoring, and integration with wearable devices for continuous assessment of heart health.