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

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
[1] Alotaibi, F. S. (2019). Implementation of machine learning model to predict heart failure disease. International Journal of Advanced Computer Science and Applications, 10 (6), 261-268.
[2] Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2018). Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics. doi: 10.1016/J.TELE.2018.11.007.
[3] Anitha, S., & Sridevi, N. (2019). Heart disease prediction using data mining techniques. Journal of Analysis and Computation, 8 (2), 48-55.
[4] Annepu, D., & Gowtham, G. (2019). Cardiovascular disease prediction using machine learning techniques. International Research Journal of Engineering and Technology, 6 (4), 3963-3971.
[5] Ayatollahi, H., Gholamhosseini, L., & Salehi, M. (2019). Predicting coronary artery disease: a comparison between two data mining algorithms. BMC Public Health. doi: 10.1186/S12889-019-6721-5.
[6] Banu, G. R., & Jamala, J. H. (2015). Heart attack prediction using data mining technique. International Journal of Modern Trends in Engineering and Research, 2 (5), 428-432.
[7] Benjamin, H., David, F., & Belcy, S. A. (2018). Heart disease prediction using data mining techniques. ICTACT Journal of Soft Computing, 9 (1), 1824-1830.
[8] Chaithra, N., & Madhu, B. (2018). Classification models on cardiovascular disease prediction using data mining techniques. Journal of Cardiovascular Diseases and Diagnosis. doi: 10.4172/2329-9517.1000348.
[9] D’Souza, A. (2015). Heart disease prediction using data mining techniques. International Journal of Research in Engineering and Science, 3 (3), 74-77.
[10] Devi, S. K. (2016). Prediction of heart disease using data mining techniques. Indian Journal of Science and Technology. doi: 10.17485/ijst/2016/v9i39/102078.
[11] Dulhare, U. N. (2018). Prediction system for heart disease using naïve bayes and particle swarm optimization. Biomedical Research, 29 (12), 2646-2649.
[12] Gawali, M., & Shirwalkar, N. (2018). Heart disease prediction system using data mining techniques. International Journal of Pure and Applied mathematics, 120 (6), 499-506.
[13] Haq, A. U., Li, J.-P., Memon, M. H., Nazir, S., & Sun, R. (2018). A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Hindawi Mobile Information System. doi: 10.1155/2018/3860146.
[14] Hariharan, K., Vigneshwar, W. S., Sivaramakrishnan, N., & Subramaniyaswamy, V. (2018). A comparative study on heart disease analysis using classification techniques. International Journal of Pure and Applied Mathematics, 119 (12), 13357-13366.
[15] Hussein, M. U. (2017, October 29). Physics and the Cardiovascular System. Retrieved from ResearchGate: https://www.researchgate.net.
[16] Jagtap, A., Malewadkar, P., Baswat, O., & Rambade, H. (2019). Heart disease prediction using machine learning. International Journal of Research in Engineering, Science and Management, 2 (2), 352-355.
[17] Kashyap, A. (2018). Artificial intelligence and medical diagnosis. Scholars Journal of Applied Medical Sciences, 4982-4985. doi: 10.21276/sjams.2018.6.12.61.
[18] Khan, S. N., Nawi, N. M., Shahzad, A., Ullah, A., & Mushtaq, M. F. (2019). Comparative analysis for heart disease prediction. International Journal on Informatics Visualization, 1 (4-2), 227-231.
[19] Khourdifi, Y., & Bahaj, M. (2018). Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. International Journal of Intelligent Engineering and Systems, 12 (1).
[20] Kim, J. K., & Kang, S. (2017). Neural network-based coronary heart disease risk prediction using feature correlation analysis. Hindawi Journal of Healthcare Engineering. doi: 10.1155/2017/2780501.
[21] Lakshmanarao, A., Swathi, Y., Sri, P., & Sundareswar, S. (2019). Machine learning techniques for heart disease prediction. International Journal of Science and Technology Research, 8 (11), 374-377.
[22] Nagendra, K. V., & Ussenaiah, M. (2018). A study on various data mining techniques used for heart diseases. International Journal of Recent Scientific Research, 24350- 24354.
[23] Nandhini, S., Debnath, M., Sharma, A., & Pushkar. (2018). Heart disease prediction using machine learning. International Journal of Recent Engineering Research and Development, 3 (10), 39-46.
[24] Narain, R., Saxena, S., & Goyal, A. K. (2016). Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach. Dovepress Journal: Patient Preference and Adherence, 10, 1259-1270.
[25] Nashif, S., Raiban, M., Islam, M., & Imam, M. H. (2018). Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system. World Journal of Engineering and Technology, 6, 854-873.
[26] Padmanabhan, M., Yuan, P., Chada, G., & Nguyen, H. V. (2019). Physician-friendly machine learning: a case study with cardiovascular disease risk prediction. Journal of Clinical Medicine. doi: 10.3390/jcm8071050.
[27] Patel, J., Upadhyay, T., & Patel, S. (2016). Heart disease prediction using machine learning and data mining techniques. IJCSC, 7 (1), 129-137.
[28] Prasad, R., Anjali, P., Adil, S., & Deepa, N. (2019). Heart disease prediction using logistic regression algorithm using machine learning. International journal of Engineering and Advanced Technology, 8 (3S), 659-662.
[29] Rabbi, M. F., Uddin, M. P., Ali, M. A., & Kibria, M. F. (2018). Performance evaluation of data mining classification techniques for heart disease prediction. American Journal of Engineering Research, 7 (2), 278-283.
[30] Raihan, M., Mondal, S., More, A., Boni, P. K., & Sagor, M. F. (2017). Smartphone based heart attack risk prediction system with statistical analysis and data mining approaches. Advances in Science, Technology and Engineering Systems Journal, 2 (3), 1815-1822.
[31] Ramalingam, V. V., Dandapath, A., & Raja, M. K. (2018). Heart disease prediction using machine learning algorithms: a survey. International Journal of Engineering and Technology, 7 (2.8), 684-687.
[32] Rammal, H., & Emam, A. Z. (2018). Toward robust heart failure prediction models using big data techniques. In Proceedings of the Tenth International Conference on e-Health, Telemedicine and Social Medicine, 85-91.
[33] Reddy, P. K., Reddy, T. S., Balakrishnan, S., Basha, S. M., & Poluru, R. K. (2019). Heart disease prediction using machine learning algorithm. International Journal of Innovative Technology and Exploring Engineering, 8 (10), 2603-2606.
[34] Ritesh, T., Gauri, B., Ashwini, D., & Priyanka, S. (2016). Heart attack prediction system using data mining. International Journal of Innovative Research in Computer and Communication Engineering, 4 (8), 15582-15585.
[35] Sabay, A., Harris, L., Bejugama, V., & Jaceldo-Siegl, K. (2018, December 24). Overcoming small data limitations in heart disease prediction by using surrogate data. Retrieved from SMU Data Science Review: https://scholar.smu.edu/datasciencereview/vol1/iss3/12.
[36] Sen, S. K. (2017). Prediction and diagnosis of heart disease using machine learning algorithms. International Journal of Engineering and Computer Science, 6 (6), 21623-21631.
[37] Shamsollahi, M., Badiee, A., & Ghazanfari, M. (2019). Using combined descriptive and predictive methods of data mining for coronary artery disease prediction: a case study approach. Journal of Artificial Intelligence and Data Mining, 7 (1), 47-58.
[38] Sharmila, S., & Gandhi, M. P. (2017). Analysis of heart disease prediction using data mining techniques. International Journal of Advanced Networking and Applications, 8 (5), 93-95.
[39] Shirsath, S. S., & Patil, S. (2018). Disease prediction using machine learning over big data. International Journal of Innovative Research in Science, Engineering and Technology, 7 (6), 6752-6757.
[40] Singh, N., & Jindal, S. (2018). Heart disease prediction system using hybrid technique of data mining algorithms. International Journal of Advanced Research, Ideas and Innovations in Technology, 4 (2), 982-987.
[41] Singh, P., Singh, S., & Pandi-Jain, G. S. (2018). Effective heart disease prediction system using data mining techniques. International Journal of Nanomedicine. doi: 10.2147IJN.S124998.
[42] Solanki, A., & Barot, M. P. (2019). Study of heart disease diagnosis by comparing various classification algorithms. International Journal of Engineering and Advanced Technology, 8 (2S2), 40-42.
[43] Sridhar, A., & Kapardhi, A. (2018). Predicting heart disease using machine learning algorithm. International Research Journal of Engineering and technology, 6 (4), 36-38.
[44] Subhadra, K., & Vikas, B. (2019). Neural network based intelligent system for predicting heart disease. International Journal of Innovative Technology and Exploring Engineering, 8 (5), 484-487.
[45] Tarawneh, M., & Embarak, O. (2019). Hybrid approach for heart disease prediction using data mining techniques. Acta Scientific Nutritional Health, 3 (7), 147-151.
[46] Unnikrishnan, P., Kumar, D. K., Arjunan, S. P., Kumar, H., Mitchell, P., & Kawasaki, R. (2016). Development of health parameter model for risk prediction of CVD using SVM. Computational and Mathematical Methods in Medicine. doi: 10.1155/2016/3016245.
[47] Voleti, S. R., & Reddi, K. K. (2016). Design of an optimal method for disease prediction using data mining techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 6 (12), 328-337.
[48] WHO. (2011). Global Atlas on cardiovascular disease prevention and control. Geneva: WHO Library Cataloguing.
[49] WHO. (2016). Technical package for cardiovascular disease management in primary health care. Geneva: WHO Library Cataloguing.
[50] WHO. (2017). Global action plan for the prevention and control of noncommunicable diseases. Geneva: WHO Library Cataloguing.
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://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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    AU  - Lamido Yahaya
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    AU  - Etemi Joshua Garba
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    T2  - American Journal of Artificial Intelligence
    JF  - American Journal of Artificial Intelligence
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    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
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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

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