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One of the top causes of mortality globally is heart disease. A doctor cannot readily foresee it since it is a complex process that needs experience and superior forecasting knowledge. The health-care institution remains "rich in information" yet "deficient in information." There is a wealth of information available in online health care systems. However, there is a scarcity of appropriate data analysis tools for detecting underlying linkages and patterns. An automated medical diagnostic system can boost medical efficiency while lowering expenses. The objective is to identify hidden patterns in cardiovascular illness using data mining techniques and forecast the existence of heart disease in individuals when presence is assessed on a scale. Predicting cardiac disease necessitates a vast quantity of data that is too complicated and voluminous for normal tools to collect and interpret. The purpose of this research is to develop the most effective and accurate machine learning approach for predicting heart disease.

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