Prediction Exploration for Coronary Heart Disease Aid of Machine Learning B. Madhuravania, Divya Priya Degalab, M. Anjaneyuluc and B.Dhanalaxmi

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B. Madhuravania et. al.


: Any machine learning approach tries to map input and output while minimizing some error metrics. In addition, machine learning techniques are not classified by setting certain cut-offs or thresholds. Once again, it is not clear how to reduce this cut-off cause, but to obtain favorable metrics. The best approach is based on metrics such as precision, recall and accuracy. Proposed work on heart disease is a major concern in today’s society. It is very difficult to physically control the chances of getting heart disease based on risk factors. However, monitoring machine-learning methods can be useful for estimating production from current data. This paper uses three different monitoring tools to diagnose and determine the best approach for coronary heart disease (chd) such as SVM, K-NN and ANN multilayer perceptron. In the case of chd, both controls have a temporary effect. Men who are positive for coronary heart disease tend to lower their risk factors after the onset of coronary heart disease through a blood pressure reduction trend and other programs. The experimental result is on three prediction methods like SVM, K-NN and ANN. It is to create and identify the coronary heart disease using three different supervise machine learning.

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