Machine Learning-Based Comparative Study For Heart Disease Prediction

Öz

Heart disease is one of the most common causes of death globally. In this study, machine learning algorithms and models widely used in the literature to predict heart disease have been extensively compared, and a hybrid feature selection based on genetic algorithm and tabu search methods have been developed. The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module. The models have been tested using different datasets, and detailed comparisons and analysis were presented. The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

Anahtar Kelimeler

• Classification
• optimization
• heart disease
• genetic algorithm
• tabu search

Kaynakça

1. Zhao X , Liu X, Su Q, Zhang M, Zhu Y, Wang Q, Wang Q. “A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method”, Hindawi Computational and Mathematical Methods in Medicine, 2017, doi: 10.1155/2017/8272091
2. Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR, et al.; on behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics–2019 update: a report from the American Heart Association. Circulation. 2019.
3. Kochanek K D, Xu J, Murphy S L, Miniño A M and Kung H C. “Deaths: final data for 2009,” National Vital Statistics Reports, vol. 60, no. 3, pp. 1–116, 2011.
4. Puntmann V O, Carerj M L, Wieters I. “Outcomes of Cardiovascular Magnetic Resonance Imaging in Patients Recently Recovered From Coronavirus Disease 2019 (COVID-19)”. JAMA Cardiol. 2020,5(11),1265–1273.
5. Wiharto W, Kusnanto H, and Herianto H. “Hybrid system of tiered multivariate analysis and artificial neural network for coronary heart disease diagnosis.” International Journal of Electrical and Computer Engineering, 7(2), (2017). 1023.
6. Amin M S, Chiam YK, and Varathan KD. “Identification of significant features and data mining techniques in predicting heart disease.” Telematics and Informatics, 36, (2019), 82-93.
7. Magesh G and Swarnalatha P. “Optimal feature selection through a cluster-based DT learning (CDTL) in heart disease prediction”, Evolutionary Intelligence, (2020), 1-11.
8. Uyar K, and İlhan A. “Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks”, Procedia computer science, 120, (2017). 588-593.

9. Latha CBC and Jeeva SC. “Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques”, Informatics in Medicine Unlocked, 16, (2019).
10. Bataineh AA, Manacek S. “MLP-PSO Hybrid Algorithm for Heart Disease Prediction”, J. Pers. Med. 2022, 12, 1208. https://doi.org/10.3390/jpm12081208
11. Hassan D, Hussein HI , Hassan MM. “Heart disease prediction based on pre-trained deep neural networks combined with principal component analysis”, Biomedical Signal Processing and Control, 2022, doi:10.1016/j.bspc.2022.104019.
12. Ahmed H, Eman MG, Younis, Hendawi A, Abdelmgeid AA, “Heart disease identification from patients’ social posts, machine learning solution on Spark”, Future Generation Computer Systems, 111, 2020, 714-722, https://doi.org/10.1016/j.future.2019.09.056.
13. Nancy AA, Ravindran D, Raj Vincent PMD, Srinivasan K, Gutierrez Reina D. “IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning”, Electronics 2022, 11, 2292. doi:10.3390/electronics11152292
14. Paul AK, Shill PC, Rabin MRI, & Akhand MAH. “Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease”, 5th International Conference on Informatics, Electronics and Vision (ICIEV) (2016). (pp. 145-150). IEEE.
15. Verma L, Srivastava S, & Negi PC. “An intelligent noninvasive model for coronary artery disease detection”, Complex & Intelligent Systems, 4(1), (2018), 11-18.
16. Kavitha R, Kannan E. “An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining”, International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS) (2016), pp. 1-5
17. Paul AK, Shill PC, Rabin MRI, Akhand MAH. “Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease” 2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016, art. no. 7759984, pp. 145-150.
18. Deng W, Zhang X, Zhou Y, Liu Y, Zhou X, Chen H, Zhao H, “An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems”, Information Sciences, Volume 585, 2022, Pages 441-453.
19. Lotf JJ, Azgomi MA, Dishabi MRZ. “An improved influence maximization method for social networks based on genetic algorithm”, Physica A: Statistical Mechanics and its Applications, Volume 586, 2022,126480.
20. Liu Y, Ćetenović D, Li H, Gryazina E, Terzija V. “An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems”, International Journal of Electrical Power & Energy Systems, Volume 136, 2022.
21. Maskooki A, Deb K, Kallio M. “A customized genetic algorithm for bi-objective routing in a dynamic network”, European Journal of Operational Research, Volume 297, Issue 2, 2022, Pages 615-629.
22. Shreem S S, Turabieh H, Al Azwari S. “Enhanced binary genetic algorithm as a feature selection to predict student performance”. Soft Comput (2022).
23. Wu H, Huang Y, Chen L, Zhu Y, Li H. “Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)”, Environmental Research, 204, Part A, 2022.
24. Karmakar R, Luhach, A K, Poonia R C, Gao X, Singh Jat D. “Application of Genetic Algorithm (GA) in Medical Science: A Review”, Second International Conference on Sustainable Technologies for Computational Intelligence, Springer Singapore, 2022, pp 83-94
25. Holland J H. “Adaptation in Natural and Artificial Systems: An İntroductory Analysis with Applications to Biology, Control, and Artificial İntelligence”. MIT Press. (1992).
26. Zhou Y, Zhang W, Kang J, Zhang X, Wang X. “A problem-specific non-dominated sorting genetic algorithm for supervised feature selection”, Information Sciences, Volume 547, 2021, Pages 841-859.
27. Abualigah L, Dulaimi AJ. “A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm”, Cluster Comput 24, 2161–2176 (2021).
28. Amini F, Hu G. “A two-layer feature selection method using Genetic Algorithm and Elastic Net”, Expert Systems with Applications, Volume 166, 2021.
29. Too J, Abdullah A.R. “A new and fast rival genetic algorithm for feature selection”, J Supercomput 77, 2844–2874 (2021).
30. Divya R, Shantha SKR. “Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification”. Neural Comput & Applic 33, 8435–8444 (2021).
31. Glover F. “Future Paths for Integer Programming and Links to Artificial Intelligence”, Computers and Operations Research, 5, (1986).533-549.
32. Hansen P. The steepest ascent mildest descent heuritic for combinatorial programming, Congress on Numerical Methods in Combinatorial Optimization, Italy. (1986).
33. Chen C, Fathi M, Khakifirooz M, Wu K. “Hybrid tabu search algorithm for unrelated parallel machine scheduling in semiconductor fabs with setup times, job release, and expired times”, Computers & Industrial Engineering, Volume 165, 2022.
34. Tong B, Wang J, Wang X, Zhou F, Mao X, Zheng W. “Optimal Route Planning for Truck–Drone Delivery Using Variable Neighborhood Tabu Search Algorithm”. Applied Sciences. 2022; 12(1):529.
35. Daneshdoost F, Hajiaghaei-Keshteli M, Sahin R, Niroomand S. “Tabu Search Based Hybrid Meta-Heuristic Approaches for Schedule-Based Production Cost Minimization Problem for the Case of Cable Manufacturing Systems”, Informatica, (2022), 1-24.
36. Schapire RE, Singer Y. “Improved boosting algorithms using confidence-rated predictions”, Mach. Learning, 37 (1999), pp. 297-336
37. Freund Y, Schapire R. “A decision-theoretic generalization of on-line learning and an application to boosting”, J. Comput. Syst. Sci., 55 (1997), pp. 119-139
38. Breiman L. “Bagging predictors.” Machine Learning, 24(2), (1996), 123–140.
39. Quinlan, JR. “Bagging, boosting, and C4.5”, Proceedings of the National Conference on Artificial Intelligence, 1(Quinlan 1993), (1996), 725–730.
40. Alan A and Karabatak M. “Veri Seti - Sınıflandırma İlişkisinde Performansa Etki Eden Faktörlerin Değerlendirilmesi”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32 (2), (2020), 531-540.