Abstract

A Comprehensive Survey on Heart Disease Prediction Using Machine Intelligence

Objectives: The latest statistics of the World Health Organization anticipated that cardiovascular diseases including Coronary Heart Disease, Heart attack, vascular disease as the biggest pandemic to the world due to which one-third of the world population would die. With the emerging AI trends, applying an optimal machine learning model to target early detection and accurate prediction of heart disease is indispensable to bring down the mortality rates and to treat cardiac patients with the best clinical decision support. This stems from the motivation of this paper. This paper presents a comprehensive survey on heart disease prediction models derived and validated out of popular heart disease datasets like the Cleveland dataset, Z-Alizadeh Sani dataset. Methods: This survey was performed using the articles extricated from the Google Scholar, Scopus, Web of Science, Research Gate, and PubMed search engines between 2005 to 2020. The main keywords for the search were Heart Disease, Prediction, Coronary disease, Healthcare, Heart datasets, and Machine Learning. Results: This review explores the shortcomings of various approaches used for the prediction of heart diseases. It outlines the pros and cons of different research methodologies along with the validation parameters of each reviewed publication. Conclusion: Machine intelligence can serve as a genuine alternative diagnostic method for prediction, which will, in turn, keep the patients well aware of their illness state. Despite the researcher’s efforts, still uncertainty exists towards the standardization of prediction models which demands further exploration of optimal prediction models.

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