Volume 25, Issue 1 (3-2022)
jha 2022, 25(1): 57-68 |
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Maleki S, Zare Mehrjerdi Y. Diagnosis of Coronary Artery Disease by Bat and Harris Hawk Meta-Heuristic Optimization Algorithms and Machine Learning Methods. jha 2022; 25 (1) :57-68
URL: http://jha.iums.ac.ir/article-1-3910-en.html
URL: http://jha.iums.ac.ir/article-1-3910-en.html
1- M.Sc student, Department of Industrial Engineering, Faculty of Industry, Yazd University, Yazd, Iran.
2- Professor, Department of Industrial Engineering, Faculty of Industry, Yazd University, Yazd, Iran. ,yzare @yazd.ac.ir
2- Professor, Department of Industrial Engineering, Faculty of Industry, Yazd University, Yazd, Iran. ,
Abstract: (1769 Views)
Introduction: Methods of detecting Coronary Artery Disease (CAD) are often prone to error and are also expensive and painful for the patient; therefore, the development and introduction of accurate machine learning-based methods for diagnosing this condition is of high importance. This research aimed to help detect coronary artery disease using the Harris Hawks Optimization (HHO) algorithm and machine learning techniques.
Methods: In this research, a novel approach based on feature selection was employed through a combination of HHO and machine learning techniques such as a Decision Tree (DT) and k-Nearest Neighbors algorithm (k-NN). To evaluate the proposed approach, we used two datasets (Cleveland & Z-Alizadeh-Sani) with medical records of 303 patients, and the evaluation was conducted by means of python 2016.
Results: On the basis of the findings of this research, feature selection by using the Harris hawks optimization algorithm in combination with machine learning methods resulted in an increase in the accuracy of the results in such a way that in the case of Z-Alizadeh-Sani dataset, the percentage of accuracy in combination with a decision tree was equal to 0.98 and in combination with the k-nearest neighbors algorithm was equal to 0.78. Furthermore, the results of the Cleveland dataset showed that using the HHO in combination with a decision tree led to 88 percent accuracy and in combination with the k-nearest neighbors algorithm led to 77 percent accuracy. However, in the case of using all of the features (HHO only mode), accuracy was lower in all cases. Therefore, the HHO algorithm in combination with the decision tree was able to achieve the highest accuracy in diagnosing CAD in the feature selection mode compared to using all of the features.
Conclusion: The results from this study showed that the Harris hawk optimization algorithm in combination with machine learning techniques can have a positive role in the process of selecting effective features in diagnosing coronary artery disease.
Methods: In this research, a novel approach based on feature selection was employed through a combination of HHO and machine learning techniques such as a Decision Tree (DT) and k-Nearest Neighbors algorithm (k-NN). To evaluate the proposed approach, we used two datasets (Cleveland & Z-Alizadeh-Sani) with medical records of 303 patients, and the evaluation was conducted by means of python 2016.
Results: On the basis of the findings of this research, feature selection by using the Harris hawks optimization algorithm in combination with machine learning methods resulted in an increase in the accuracy of the results in such a way that in the case of Z-Alizadeh-Sani dataset, the percentage of accuracy in combination with a decision tree was equal to 0.98 and in combination with the k-nearest neighbors algorithm was equal to 0.78. Furthermore, the results of the Cleveland dataset showed that using the HHO in combination with a decision tree led to 88 percent accuracy and in combination with the k-nearest neighbors algorithm led to 77 percent accuracy. However, in the case of using all of the features (HHO only mode), accuracy was lower in all cases. Therefore, the HHO algorithm in combination with the decision tree was able to achieve the highest accuracy in diagnosing CAD in the feature selection mode compared to using all of the features.
Conclusion: The results from this study showed that the Harris hawk optimization algorithm in combination with machine learning techniques can have a positive role in the process of selecting effective features in diagnosing coronary artery disease.
Type of Study: Research |
Subject:
Health Information Management
Received: 2021/12/18 | Accepted: 2022/03/19 | Published: 2022/07/13
Received: 2021/12/18 | Accepted: 2022/03/19 | Published: 2022/07/13
References
1. Reddy KS. Cardiovascular diseases in the developing countries: Dimensions, determinants, dynamics and directions for public health action. Public Health Nutr. 2002;5(1a):231-7. [DOI:10.1079/PHN2001298]
2. Squeri A. Coronary artery disease - new insights and novel approaches [Internet]. London: IntechOpen; 2012 [cited 2022 Apr 30]. Available from: https://www.intechopen.com/books/660 [DOI:10.5772/1168]
3. Nahar J, Imam T, Tickle KS, Phoebe Chen YP. Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst Appl. 2013;40(4):1086-93. [DOI:10.1016/j.eswa.2012.08.028]
4. Giri D, Acharya UR, Martis RJ, Sree SV, Lim TC, VI TA, et al. Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl Base Syst. 2013;37:274-82. [DOI:10.1016/j.knosys.2012.08.011]
5. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications. Future Generat Comput Syst. 2019;97:849-72. [DOI:10.1016/j.future.2019.02.028]
6. Rani KU. Analysis of heart diseases dataset using neural network approach. International Journal of Data Mining & Knowledge Management Process. 2011;1(5):1-8. [DOI:10.5121/ijdkp.2011.1501]
7. DezhAloud N, Soleimanian Gharehchopogh F. Diagnosis of heart disease using binary Grasshopper optimization algorithm and K-Nearest neighbors. Journal of Health Administration. 2020;23(3):42-54. [In Persian] [DOI:10.29252/jha.23.3.42]
8. Vila-Frances J, Sanchis J, Soria-Olivas E, Serrano AJ, Martinez-Sober M, Bonanad C, et al. Expert system for predicting unstable angina based on Bayesian networks. Expert Syst Appl. 2013;40(12):5004-10. [DOI:10.1016/j.eswa.2013.03.029]
9. Abdar M, Ksiazek W, Acharya UR, Tan RS, Makarenkov V, Pławiak P. A new machine learning technique for an accurate diagnosis of coronary artery disease. Comput Methods Programs Biomed. 2019;179:1-11. [DOI:10.1016/j.cmpb.2019.104992]
10. Al-Tashi Q, Rais H, Jadid S. Feature selection method based on grey wolf optimization for coronary artery disease classification. In: Saeed F, Gazem N, Mohammed F, Busalim A, editors. Recent trends in data science and soft computing. IRICT 2018. Advances in intelligent systems and computing. Kuala Lumpu: Springer; 2019 Sep. p. 257-66. [DOI:10.1007/978-3-319-99007-1_25]
11. Data Sets - UCI Machine Learning Repository [Internet]. Amherst:The University of Massachusetts Amherst; c2007- [cited 2021 Feb]. Available from: https: //archive.ics.uci.edu/ml/datasets.php
12. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications. Future Gener Comput Syst. 2019;97:849-72. [DOI:10.1016/j.future.2019.02.028]
13. Balamurugan R, Ratheesh S, Venila YM. Classification of heart disease using adaptive Harris hawk optimization-based clustering algorithm and enhanced deep genetic algorithm. Soft comput. 2022;26:2357-73. [DOI:10.1007/s00500-021-06536-0]
14. Coomans D, Massart DL. Alternative k-nearest neighbour rules in supervised pattern recognition: Part 1. k-Nearest neighbour classification by using alternative voting rules. Anal Chim Acta. 1982;136:15-27. [DOI:10.1016/S0003-2670(01)95359-0]
15. Ray S. Understanding support vector machine (SVM) algorithm from examples (along with code) [Internet]. Analytics Vidhya, 2015 Oct [updated 2017 Sep]. Available from: https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
16. Sapra L, Sandhu JK, Goyal N. Intelligent method for detection of coronary artery disease with ensemble approach. In: Hura G, Singh A, Siong Hoe L, editors. Advances in communication and computational technology. Singapore: Springer; 2021. p. 1033-42. [DOI:10.1007/978-981-15-5341-7_78]
17. Vijayashree J, Sultana HP. A machine learning framework for feature selection in heart disease classification using improved particle swarm optimization with support vector machine classifier. Program Comput Software. 2018;44(6):388-97. [DOI:10.1134/S0361768818060129]
18. Shouman M, Turner T, Stocker R. Using decision tree for diagnosing heart disease patients. Proceedings of the Ninth Australasian Data Mining Conference. 2011 Dec 1-2;Ballarat Australia. 2011. p. 23-30.
19. Nguyen T, Khosravi A, Creighton D, Nahavandi S. Medical data classification using interval type-2 fuzzy logic system and wavelets. Appl Soft Comput. 2015;30:812-22. [DOI:10.1016/j.asoc.2015.02.016]
20. Maleki S, Zare Mehrjerdi Y, shishebori D, Mirzaei M. Predicting coronary artery diseases using effective features selected by Harris Hawks optimization algorithm and support vector machine. Journal of Industrial and Systems Engineering. 2022 Jan;14:40-47.
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