An Approach with Machine Learning for Heart Disease Risk Prediction

Heart disease is a prominent cause of death worldwide, needing novel techniques for early detection and care. This study looks into the potential of machine learning in predicting heart illness and addresses the limitations of existing risk assessment approaches. To ensure data quality, large, high-quality datasets are collected, and data preparation techniques are used. For heart disease risk prediction, several machine learning methods are used, with an emphasis on feature selection and engineering. The study underlines the necessity of collaboration among healthcare practitioners, data scientists, and patients in addressing data quality, privacy, and ethical concerns. The outcomes of this study show that machine learning has the potential to improve risk assessment, early identification, and individualized therapy of heart disease. Machine learning is critical in the identification of arrhythmias, image analysis, individualized treatment strategies, and medication development. Machine learning models in clinical decision support systems can enhance patient care and outcomes. Despite the encouraging results, the study recognizes issues such as data quality, class imbalance, model interpretability, and privacy concerns. Ethical issues, clinical validation, and regulatory compliance are also important factors to consider when applying machine learning in healthcare. The study emphasizes the need to work together to fully realize the promise of machine learning, assuring improved patient outcomes and lowering the global burden of cardiovascular disease.