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

Lamido Yahaya; Nathaniel David Oye; Etemi Joshua Garba

doi:doi:10.11648/j.ajai.20200401.12

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

Lamido Yahaya, Nathaniel David Oye, Etemi Joshua Garba

Published in American Journal of Artificial Intelligence (Volume 4, Issue 1)

Received: 12 March 2020 Accepted: 2 April 2020 Published: 23 April 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.

Published in	American Journal of Artificial Intelligence (Volume 4, Issue 1)
DOI	10.11648/j.ajai.20200401.12
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), 2020. Published by Science Publishing Group

Keywords

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

References

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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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques
AU  - Lamido Yahaya
AU  - Nathaniel David Oye
AU  - Etemi Joshua Garba
Y1  - 2020/04/23
PY  - 2020
N1  - https://doi.org/10.11648/j.ajai.20200401.12
DO  - 10.11648/j.ajai.20200401.12
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 20
EP  - 29
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20200401.12
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
IS  - 1
ER  -

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Author Information

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

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Plain Text BibTeX RIS

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques
AU  - Lamido Yahaya
AU  - Nathaniel David Oye
AU  - Etemi Joshua Garba
Y1  - 2020/04/23
PY  - 2020
N1  - https://doi.org/10.11648/j.ajai.20200401.12
DO  - 10.11648/j.ajai.20200401.12
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 20
EP  - 29
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20200401.12
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
IS  - 1
ER  -

Copy | Download