Towards Automated Diagnosis of Heart Diseases: A Study of Machine Learning Techniques

The study applies machine learning algorithms to the diagnosis of heart disease. Data were collected from multiple sources in hospital and clinic records, along with time-based comparison other studies. The second part of the study, Clinical Decision Support, simulated the daily work of a physician and helped them make patient-centered medical decisions. The results revealed significant potential for machine learning to improve heart disease detection efficiency and accuracy, which could benefit future effective disease management and reduce patient burden. The study findings will enable future healthcare providers to harness new technology to achieve better prevention and superior care outcomes for heart disease screening. The study recommendations include optimal diagnostic skills and intervention-oriented preventive measures.