Most of the existing Clinical Decision Support Systems(CDSS) for predicting Coronary Heart Disease(CHD) risks rely solely on the analysis of single type of diagnostic data or a certain type of classification algorithm, thus making the predicted component unreliable. Since the Risk value very much influences the further course of the treatment, the correctness of the risk value is eminent. So the novelty of the proposed method lies in considering many classification algorithms and many diagnostics data sets to predict the risk value. Moreover in the proposed method we have given flowchart for finding the risk value, which is a novelty. The results shows that the selection of classification algorithms cannot be universal as proposed by many recent researches and each type of data set will be having a certain type of classifier which works best for the data set. The proposed system has four classifiers namely Mixed cardio data classifier, Single Photon Emission Computed Tomography(SPECT) data classifier, Electrocardiogram(ECG) data classifier and 1-year survival Electrocardiogram data classifier, each trained with benchmark datasets of University of California, Irvine(UCI) Machine learning repository. The performance of algorithms such as Iterative Dichotomiser3(ID3), Naïve Bayes(NB), Support Vector Machine(SVM), k-Nearest Neighbors(kNN) and Ensemble classifiers such as Bagging and Boosting are evaluated for every data set and best classifier is chosen by its accuracy measure. Ensemble method is the most efficient for 1-year survival ECG data classifier with 89.5% accuracy, ID3 is the most efficient algorithm for Mixed cardiac data classifier with 96.9% accuracy and SVM is the most efficient algorithm for SPECT data classifier and ECG data Classifier with accuracy of 98.3% and 100% respectively. The proposed work also finds the optimal number of samples needed for Bagging and Boosting type of ensemble classifiers.