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Prediction methods based on machine learning for clinical diagnosis are attaining a magnified significance over decade. This might be a scope of these methods for assisting medical-practitioners without committing fake diagnosis during phase of medical training. Nevertheless, the learning methods would act as prominent role in estimating diseases during early phase. Other important clinical data learning methods research objective is process and its computational complexity. Thus, this chapter projected a learning method, which devises measured thresholds from specified training set that is utilized further for labeling specified patient records could be prone towards heart disease or not. Simulation results exhibit that devised prediction method provides an optimum performance towards an accuracy of prediction.