As big data becomes more prevalent in the healthcare and medical sectors, accurate medical data collection benefits early diagnosis of heart disease, hospital treatment, and government resources. However, where medical data quality is lacking, understanding accuracy suffers. Consequently, some field diseases have unique features in different regions, which can make illness more difficult. It is now more hard to predict outbreaks. We automate machine learning algorithms for efficient epidemic detection in bacterial infection population in this paper. We put the modified forecasts to the test using securely and efficiently datasets. areas of the region to improve the situation of lost data, we use a predictive modeling approach to restore inaccurate value. Focused upon its patient's signs, a heart attack is suspected. Models were built using machine learning techniques. As a consequence, the accuracy is pinpoint accurate. The Flask web interface is used to build the Application. In this research, we shall conduct experiments using machine learning methods.

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