Predicting coronary artery diseases using effective features selected by Harris Hawks optimization algorithm and support vector machine

Document Type : conference paper


1 Department of Industrial Engineering, Technical Engineering Faculty, Yazd University, Yazd, Iran

2 Disease Modeling Center of Shahid Sadoughi University of Medical Sciences, Yazd, Iran


With 17 million annual deaths, cardiovascular diseases are the leading cause of mortality across the world with coronary artery disease (CAD) as the most prevalent one. CAD is the leading cause of death in industrial countries and at the same time is rapidly spreading in the developing world. Thus, the development and introduction of machine learning methods for the accurate diagnosis of heart diseases, especially CAD, have been an important debate in recent years in order to overcome relevant problems. The aim of this paper was to propose a model for enhancing CAD prediction accuracy. It sought a framework for predicting and diagnosing CAD using the features selection of Harris Hawks Optimization algorithm (HHO) and Support Vector Machine (SVM). The heart disease data set of Cleveland hospital available in the University of California Irvine (UCI) was used as the studied data set. It included 303 cases. Each case had 14 features with the final medical status of cases (CAD or normal case) as one of the features where 165 and 138 cases were diagnosed as CAD and normal, respectively. The results of this study revealed that HHO could enhance CAD diagnosis accuracy.


Main Subjects

Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., & Pławiak, P. (2019). A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179, 104992.
DezhAloud, N. (2020). Diagnosis of Heart Disease Using Binary Grasshopper Optimization Algorithm and K-Nearest Neighbors. Journal of Health Administration, 23(3), 42-54.
Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley & Sons.
Glaros, A. G., & Kline, R. B. (1988). Understanding the accuracy of tests with cutting scores: The sensitivity, specificity, and predictive value model. Journal of clinical psychology, 44(6), 1013-1023.
Giri, D., Acharya, U. R., Martis, R. J., Sree, S. V., Lim, T. C., VI, T. A., & Suri, J. S. (2013). Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowledge-Based Systems, 37, 274-282.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
Khosravanian, A., & Ayat, S. S. (2015). Presenting an intelligent system for diagnosis of coronary heart disease by using Probabilistic Neural Network.
Nahar, J., Imam, T., Tickle, K. S., & Chen, Y. P. P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4), 1086-1093.
Nasarian, E., Abdar, M., Fahami, M. A., Alizadehsani, R., Hussain, S., Basiri, M. E., ... & Sarrafzadegan, N. (2020). Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach. Pattern Recognition Letters, 133, 33-40.
Ndindjock, R., Gedeon, J., Mendis, S., Paccaud, F., & Bovet, P. (2011). Potential impact of single-risk-factor versus total risk management for the prevention of cardiovascular events in Seychelles. Bulletin of the World Health Organization, 89, 286-295.
Negahbani, M., Joulazadeh, S., Marateb, H. R., & Mansourian, M. (2015). Coronary artery disease diagnosis using supervised fuzzy c-means with differential search algorithm-based generalized Minkowski metrics. Peertechz Journal of Biomedical Engineering, 1(1), 006-014.
Rani, K. U. (2011). Analysis of heart diseases dataset using neural network approach. arXiv preprint arXiv:1110.2626.
Reddy, K. S. (2002). Cardiovascular diseases in the developing countries: dimensions, determinants, dynamics and directions for public health action. Public health nutrition, 5(1a), 231-237.
Vila-Francés, J., Sanchis, J., Soria-Olivas, E., Serrano, A. J., Martinez-Sober, M., Bonanad, C., & Ventura, S. (2013). Expert system for predicting unstable angina based on Bayesian networks. Expert systems with applications, 40(12), 5004-5010.