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Machine Learning in medical health care is evolving as a significant research field for delivering prognosis and a deeper understanding of medical data. Most methods of machine learning depend on several features defining the behaviour of the algorithm, influencing the output, and the complexity of the resulting models either directly or indirectly. Many machine learning methods have been used in the past to detect heart diseases. Neural network and logistic regression are some of the few popular machine learning methods used in heart disease diagnosis. They analyse multiple algorithms such as Support vector machine, K-nearest neighbour, Random Forest classifier, along with composite approaches incorporating the aforementioned heart disease diagnostic algorithms. The system was implemented and trained in the python platform by using the machine learning model. For the new data collection, the framework can be extended.