Abstract: One of the most common tasks inmachine learning is to classify data. Machine learning is a key feature to derive information from corporate operating datasets from large databases. Machine Learning in Medical Health Care is an essential emerging field for delivering prognosis anda deeper understanding of medical data. Most methods of machine learning depend on several features defining the behavior of the algorithm and influencing the output and the complexity of the resulting models directly or indirectly. In the last tenyears, heart disease is the world’s leading cause of death. Many machine learning methods have been used in the past to detect heart disease. Neural Network and Logistic Regression are some of the fewpopular machine learning methods used in heart disease diagnosis. They analyze multiple algorithms such as Neural Network, KNearest Neighbors, NaiveBayes, and Logistic Regression along withcomposite approaches incorporating the above-mentioned heart disease diagnostic algorithms. The system was implemented and trained in the Python platform using the UCI machine learning repository benchmark dataset. For the new data collection, the framework can be extended.