Machine learning algorithms using for heart disease prediction
Author(s): Swetha Bojji Reddy
Abstract: There is such a great amount of information in the social insurance field that specific techniques can be utilized to process that information. Information mining is one of the most much of the time utilized techniques. Heart disease is the main source of death around the world. This framework evaluates the odds of heart disease. The aftereffects of this framework offer a level of heart disease hazard. The datasets utilized are ordered by clinical boundaries. This framework utilizes the information mining order technique to assess those boundaries. Datasets are prepared in Python programming utilizing two fundamental AI calculations, the Decision Tree calculation and the Naive Bayes calculation, which show the best calculation of both regarding exactness of heart disease.