When it comes to the identification of diseases, clinical science is often biased. Several diseases in clinical science can lead to death due to a lack of timely prediction. Among these diseases, cardiac illness is one of them. Approximately one-third of the world population is suffered by cardiac disorder. At present physicians refer different test reports like ECG, and ECO and then use their experience for the detection of cardio vascular disease. But the prediction based on these reports is not always correct. This inaccurate diagnosis mostly results in the demise of the patent. In this research, three types of boosting-based classifiers are applied for the better prediction of cardiac illness. The dataset for the proposed system is the collection of the clinical database of different geographical included in the UCI repository. Among all the proposed classifiers Gradient boosting has shown the best accuracy of 97.62%.

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