American Journal of Artificial Intelligence

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A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques

Received: Mar. 12, 2020    Accepted: Apr. 02, 2020    Published: Apr. 23, 2020
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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.

DOI 10.11648/j.ajai.20200401.12
Published in American Journal of Artificial Intelligence ( Volume 4, Issue 1, June 2020 )
Page(s) 20-29
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Mining, Machine Learning, Heart Disease, Classification, Prediction

References
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Cite This Article
  • APA Style

    Lamido Yahaya, Nathaniel David Oye, Etemi Joshua Garba. (2020). A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques. American Journal of Artificial Intelligence, 4(1), 20-29. https://doi.org/10.11648/j.ajai.20200401.12

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    ACS Style

    Lamido Yahaya; Nathaniel David Oye; Etemi Joshua Garba. A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques. Am. J. Artif. Intell. 2020, 4(1), 20-29. doi: 10.11648/j.ajai.20200401.12

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    AMA Style

    Lamido Yahaya, Nathaniel David Oye, Etemi Joshua Garba. A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques. Am J Artif Intell. 2020;4(1):20-29. doi: 10.11648/j.ajai.20200401.12

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  • @article{10.11648/j.ajai.20200401.12,
      author = {Lamido Yahaya and Nathaniel David Oye and Etemi Joshua Garba},
      title = {A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques},
      journal = {American Journal of Artificial Intelligence},
      volume = {4},
      number = {1},
      pages = {20-29},
      doi = {10.11648/j.ajai.20200401.12},
      url = {https://doi.org/10.11648/j.ajai.20200401.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajai.20200401.12},
      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.},
     year = {2020}
    }
    

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    T2  - American Journal of Artificial Intelligence
    JF  - American Journal of Artificial Intelligence
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    AB  - 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.
    VL  - 4
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Author Information
  • Department of Computer Science, Faculty of Science, Gombe State University, Gombe, Nigeria

  • Department of Computer Science, School of Physical Sciences, Modibbo Adama University of Technology, Yola, Nigeria

  • Department of Computer Science, School of Physical Sciences, Modibbo Adama University of Technology, Yola, Nigeria

  • Section