Publication

Abstract : The healthcare environment comprises the enormous amount of data such as clinical information, genetic data, and data generated from electronic health records (EHR). Machine learning, Data mining and deep learning methods provide the methodology and technology to extract valuable knowledge for decision making. Heart disease (HD) is one of the cardiovascular diseases which are diseases of the heart and blood vessel system. Extensive research in all aspects of heart disease (diagnosis, therapy, ECG, ECHO etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, Deep learning techniques, and tools in the field of Heart disease research with respect to Heart disease complications, Prediction, and diagnosis. In general, 60% of those used were characterized by machine learning techniques and support vector machines and 30% by deep learning approaches. Most of the data used are Clinical datasets. From this survey, it provides insights in electing suitable algorithms and methods to improve accuracy in HD prediction. The selected articles in this study projected in extracting useful knowledge accelerated new hypothesis targeting deeper understanding and further investigation in cardiovascular disease.