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Leveraging The Power of Hybrid Machine Learning Algorithms to Predict Cardiovascular Diseases - A Review

Anuradha. P*, VASANTHA KALYANI DAVID**

* Research Scholar, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Tamil Nadu, India.

** Professor, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Tamil Nadu, India.

Periodicity:September - November'2017
DOI : https://doi.org/10.26634/jcom.5.3.14018

Abstract

As people are becoming more health conscious, preventive health care is gaining importance over diagnostic health care. The goal of future medicine is to provide personalized medical care. According to World Health Organization (WHO), 31% of all global deaths are due to Cardiovascular Diseases (CVDs). In order to prevent heart diseases, the unexplored hidden information in the health care data can be efficiently obtained by applying hybrid Machine Learning Algorithms. These algorithms would help the medical practitioners to gain insight into higher dimensional data, thereby assisting them to predict cardiac arrests even before it occurs. This would enhance medical care and reduce costs for patients. This paper surveys and highlights on the suitable statistical and hybrid Machine Learning Algorithms used for feature selection, prediction, and performance evaluation.

Keywords

Machine Learning, Cardio Vascular Diseases, SVM, GA, PSO, KNN.

How to Cite this Article?

Anuradha P., and David, V.K. (2017). Leveraging The Power of Hybrid Machine Learning Algorithms to Predict Cardiovascular Diseases - A Review. i-manager’s Journal on Computer Science, 5(3), 60-67. https://doi.org/10.26634/jcom.5.3.14018

References

[1]. Ahmed, A., & Hannan, S. A. (2012). Data Mining Techniques to find out heart diseases: An overview. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 1(4), 18-23.

[2]. Alpaydin, E. (2014). Introduction to Machine Learning. MIT Press.

[3]. Amin, S. U., Agarwal, K., & Beg, R. (2013, April). Genetic neural network based data mining in prediction of heart disease using risk factors. In Information & Communication Technologies (ICT), 2013 IEEE Conference on (pp. 1227-1231). IEEE.

[4]. Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A. (2017). Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Computer Methods and Programs in Biomedicine, 141, 19-26.

[5]. Austin, P. C., Tu, J. V., Ho, J. E., Levy, D., & Lee, D. S. (2013). Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes. Journal of Clinical Epidemiology, 66(4), 398-407.

[6]. Bisaso, K. R., Anguzu, G. T., Karungi, S. A., Kiragga, A., & Castelnuovo, B. (2017). A survey of machine learning applications in HIV clinical research and care. Computers in Biology and Medicine, 91, 366-371.

[7]. Chaurasia, V. (2017). Early prediction of heart diseases using data mining techniques. Carib. J. Sci. Tech., 1,208-217.

[8]. Fujita, H., Acharya, U. R., Sudarshan, V. K., Ghista, D. N., Sree, S. V., Eugene, L. W. J., & Koh, J. E. (2016). Sudden Cardiac Death (SCD) prediction based on nonlinear heart rate variability features and SCD index. Applied Soft Computing, 43, 510-519.

[9]. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.

[10]. Iftikhar, S., Fatima, K., Rehman, A., Almazyad, A. S., & Saba, T. (2017). An evolution based hybrid approach for heart diseases classification and associated risk factors identification. Biomedical Research, 28(8), 3451-3455.

[11]. Inbarani, H. H., Azar, A. T., & Jothi, G. (2014). Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Computer Methods and Programs in Biomedicine, 113(1), 175-185.

[12]. Jabbar, M.A., Deekshatulu, B. L., & Chandra, P. (2013). Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technology, 10, 85-94.

[13]. Kim, J., Kang, U., & Lee, Y. (2017). Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction. Healthcare Informatics Research, 23(3), 169-175.

[14]. Long, N. C., Meesad, P., & Unger, H. (2015). A highly accurate firefly based algorithm for heart disease prediction. Expert Systems with Applications, 42(21), 8221-8231.

[15]. Machine Learning with MATLAB. (n.d.). In MathWorks. Retrieved from https://in.mathworks.com/solutions/ machine-learning.

[16]. Mahmoodabadi, Z., & Tabrizi, S. S. (2015). A new efficient algorithm based on ICA for diagnosis of Coronary Artery Disease. International Journal of Telemedicine and Clinical Practices, 1(2), 157-173.

[17]. Masethe, H. D., & Masethe, M. A. (2014, October). Prediction of heart disease using classification algorithms. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 2, pp. 22-24).

[18]. Mustaqeem, A., Anwar, S. M., Khan, A. R., & Majid, M. (2017). A statistical analysis based recommender model for heart disease patients. International Journal of Medical Informatics, 108, 134-145.

[19]. Parthiban, L., & Subramanian, R. (2008). Intelligent heart disease prediction system using CANFIS and genetic algorithm. International Journal of Medical and Health Sciences, 1(5), 278-281.

[20]. Patel, S. B., Yadav, P. K., & Shukla, D. D. (2013). Predict the diagnosis of heart disease patients using classification mining techniques. IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS), 4(2), 61-64.

[21]. Paul, A. K., Shill, P. C., Rabin, M. R. I., & Murase, K. (2017). Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Applied Intelligence, 48, 1-18.

[22]. Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507-2517.

[23]. Shouman, M., Turner, T., & Stocker, R. (2011, December). Using decision tree for diagnosing heart disease patients. In Proceedings of the Ninth Australasian Data Mining Conference (Vol.121, pp. 23-30). Australian Computer Society, Inc.

[24]. Shouman, M., Turner, T., & Stocker, R. (2012, March). Using data mining techniques in heart disease diagnosis and treatment. In Electronics, Communications and Computers (JEC-ECC), 2012 Japan-Egypt Conference on (pp. 173-177). IEEE.

[25]. Singh, J., Kamra, A., & Singhra, H. (2016). Prediction th of Heart Diseases using Associative Classification. 5 International Conference on Wireless Networks and Embedded Systems (WECON) (pp. 1-7). IEEE.

[26]. Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 43-48.

[27]. Srinivas, K., Rani, B.K., Govardhan, A. (2010a). Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks. International Journal on Computer Science and Engineering, 2(2), 250-255.

[28]. Srinivas, K., Rao, G. R., & Govardhan, A. (2010b, August). Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques. In Computer Science and th Education (ICCSE), 2010 5 International Conference on (pp. 1344-1349). IEEE.

[29]. Srinivas, K., Rao, G. R., & Govardhan, A. (2014). Rough-Fuzzy classifier: A system to predict the heart disease by blending two different set theories. Arabian Journal for Science and Engineering, 39(4), 2857-2868

[30]. Tayefi, M., Tajfard, M., Saffar, S., Hanachi, P., Amirabadizadeh, A. R., Esmaeily, H., & Mobarhan, M. G. (2017). hs-CRP is strongly associated with Coronary Heart Disease (CHD): A data mining approach using decision tree algorithm. In Computer Methods and Programs in Biomedicine, 141, 105–109.

[31]. Tu, M. C., Shin, D., & Shin, D. (2009, October). Effective diagnosis of heart disease through bagging approach. In Biomedical Engineering and Informatics, nd 2009. BMEI'09. 2 International Conference on (pp. 1-4). IEEE.

[32]. UCI Machine Learning Repository-Heart Disease Dataset. Retrieved from https://archive.ics.uci.edu/ml/ datasets/Heart+ Disease

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