The condition that is most prevalent nowadays is heart disease, that may be successfully treated if caught and treated at an early enough stage. Heart disease diagnosis requires extreme caution since the procedure might be derailed by human mistake. Machine learning techniques were widely popular in many walks of life, but they rose to prominence in the field of heart disease forecasting. Many biological characteristics included in cardiac patient datasets have little bearing on diagnosis. Prediction accuracy for cardiac patients may be improved while computational complexity is reduced by eliminating irrelevant elements from the available data-set. This technique provides a density-based unsupervised method for identifying cardiac anomalies. The filter-based feature selection strategy is used to begin the process of narrowing down the data collection to its most fundamental characteristics. In order to improve the clustering effectiveness of healthy cases and to detect aberrant examples like cardiac patients, a new method for clustering with adaptive variables called Density Based Clustering has been applied. The DBSCAN method, that generates density-based clusters, is intended to solve these problems; though, the best way to choose an epsilon value and a minimum value is still up for debate. These two factors are used in the suggested strategy to achieve high diagnostic accuracy in patients with cardiac conditions.

Heart Diseases, Diseases Prediction Model, Outlier Data, Machine Learning

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