Exploration of Different Techniques on Heart Disease Prediction

This original review offers a concise overview of heart disease prediction using machine learning and data science. It acknowledges the ongoing global health challenge posed by cardiovascular diseases (CVDs) and the imperative for accurate risk assessment and early detection. The paper explores the role of machine learning, discusses data sources such as electronic health records and genomic data, and covers a range of algorithms. It addresses challenges like data imbalances and privacy concerns and emphasizes essential performance metrics. Ethical considerations, including patient privacy and model transparency, are also discussed, offering a roadmap for future research in this critical area.