Abstract

AbstractHeart disease is a common condition that may be fatal in older people and those who don't lead healthy lifestyles. They may somewhat avoid it with regular diagnostic tests, proper eating habits, and regular checkups. A lot of patient data is produced by hospitals, including x-rays, lung tests, heart pain tests, chest pain tests; personal health records (PHRs), and so on. The symptoms—more precisely, the qualities needed for prediction—are used to create the decision tree classifier. Using the decision tree approach, we can pinpoint specific characteristics that are the best and result in a better prediction of the datasets. Hospital data collection is not being used effectively. Some of these technologies are utilized to get information from the heart disease detection database, whileother uses are prohibited. This study determines whether or not individuals have cardiac ailments based on the data in their records by using a optimization techniques Machine Learning algorithms, and health care data. To determine if the patient has heart illness, try using the data as a model.