Search

Life style disorders like diabetes, cardio vascular diseases (CVD) etc. are spreading like an epidemic to a pandemic. Heart disease (HD) besides other health consequences has serious social and economical implications for the individual and for the country. These conditions have multiple dimensions and can be controlled and prevented, if diagnosed and treated in time, by improving the overall personality/life style of an individual with the help of technology. It has been found from the literature that a huge number and combination of algorithms/classifiers with great performances/accuracies have already been employed to predict the heart disease (HD). SVM classifier has been predominantly deployed for classification. Neural networks (NNs) and ensembles have also been used which have also yielded high accuracy of over 95%. CNNs and deep learning techniques too have yielded significantly high accuracies of over 95% recently. It has already been established that ensemble techniques/models consisting of a combination of several related but different models like bagging, boosting, blending/stacking and Majority voting have performed better on (Cleveland) datasets with 91.6% accuracy than individual constituent models/classifiers-called base learners. Some ensemble algorithms/models like Random Forest algorithm have been found to have achieved 100% accuracy for heart disease prediction. Further, ensemble models solve the problems like over fitting etc. also and are more suitable for the imbalanced data sets like medical/heart datasets. Owing to the unique feature processing capabilities for processing big data which is heterogeneous, high dimensional, multivariate unstructured and unlabelled in nature, deep learning deals best with unstructured and unlabelled data i.e. unsupervised learning and do not need much data pre-processing in comparison with the data mining/machine learning and does not require the processes of filtering, normalization etc. that are manually done in case of machine learning. This review paper aims at analyzing the literature to review and compare different techniques utilized for HD prediction to identify the best base learner so that in future an effective deep learning based ensemble model could be proposed that can be applied for real world heart disease data sets with improved performance. [ABSTRACT FROM AUTHOR]