Role of Machine Learning In Health Care System for The Prediction of Different Diseases

Machine Learning (ML) refers to a variety of statistical approaches that enable computers to learn from experience without being explicitly programmed. This learning generally manifests itself as modifications to how an algorithm operates. For physicians, predicting and identifying heart illness has always been a challenging and time-consuming task. Hospital and other institutions provide pricey operations and treatments to treat hearing issues. Early heart disease identification will help people all around the globe since it will enable them to get the proper care before it worsens. Overconsumption of alcohol, cigarette smoking, and inactivity have been the main contributors to heart disease in recent years. The healthcare industry has used machine learning to generate predictions and decisions from a significant quantity of data across time. The supervised machine learning techniques used in this prediction of coronary heart disease include artificial neural network (ANN), decision trees (DT), Random Forest (RF), support vector systems (SVM), nave Boltzmann (NB), and nearest neighbour algorithm. Additionally, a summary of the outcomes from several algorithms is provided.