Using machine learning to predict heart disease

One of the hardest problems facing the medical sector today is predicting cardiac disease. In the current day, almost one person per minute passes away from heart disease. Data science is needed in the healthcare sector to process enormous amounts of data. Automating the procedure is crucial to reduce hazards and advise the patient well in advance because predicting cardiac sickness is a challenging undertaking. This work makes use of the UCI machine learning repository's dataset on heart illness. The suggested system uses a variety of data mining approaches, such as the Naive Bayes algorithm, Decision Trees classifier, Logistic Regression, and Random Forest classifier, to predict the likelihood of heart disease and categorise patient risk levels. As a result, comparative study is presented in this work by analysing the efficacy of several machine learning algorithms.