Volume 4, Issue 1, June 2020, Page: 20-29
A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques
Lamido Yahaya, Department of Computer Science, Faculty of Science, Gombe State University, Gombe, Nigeria
Nathaniel David Oye, Department of Computer Science, School of Physical Sciences, Modibbo Adama University of Technology, Yola, Nigeria
Etemi Joshua Garba, Department of Computer Science, School of Physical Sciences, Modibbo Adama University of Technology, Yola, Nigeria
Received: Mar. 12, 2020;       Accepted: Apr. 2, 2020;       Published: Apr. 23, 2020
DOI: 10.11648/j.ajai.20200401.12      View  426      Downloads  572
Abstract
Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today’s digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.
Keywords
Data Mining, Machine Learning, Heart Disease, Classification, Prediction
To cite this article
Lamido Yahaya, Nathaniel David Oye, Etemi Joshua Garba, A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques, American Journal of Artificial Intelligence. Vol. 4, No. 1, 2020, pp. 20-29. doi: 10.11648/j.ajai.20200401.12
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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