CLASSIFICATION OF CORONARY ARTERY DISEASES USING STACKING ENSEMBLE LEARNING METHOD

Yıl 2020, Cilt: 5 Sayı: 2, 69 - 73, 31.12.2020

Adem Doğaner , Mehmet Kirişçi

Öz

Aim: Coronary artery disease is one of the most fatal diseases in the the society. Early diagnosis and treatment of coronary artery disease plays an important role in reducing the number of deaths. In this study, it is aimed to classify coronary artery disease by Stacking based ensemble learning methods.
Material and Methods: The study was obtained from the data of 244 patients with coronary artery disease and 116 individuals without coronary artery disease who were treated in Kahramanmaras Sutcu Imam University Health Practice and Research Hospital. The data were obtained retrospectively. The data set consists of 15 predictor variables and 1 dependent variable. In the classification process, Naive Bayes, Sequential Minimal Optimization, Random Forest classifiers and Stacking ensemble learning method were applied. A 10-fold cross validation method was applied to the model. Accuracy, sensitivity, specificity, F-measure and AUC metrics were applied to evaluate the performance of classifiers. The most essential variables in predicting coronary artery disease have been determined.
Results: ACC = 0.774, SEN = 0.888, SPE = 0.719, F = 0.718 and AUC = 0.913 values were obtained with the Naive Bayes classifier in the study. ACC = 0.883, SEN = 0.733, SPE = 0.955, F = 0.802 and AUC = 0.844 were obtained with the SMO classifier. ACC = 0.908, SEN = 0.853, SPE = 0.934, F = 0.857 and AUC = 0.957 were obtained with Random Forest classifier. ACC = 0.933, SEN = 0.905, SPE = 0.946, F = 0.897 and AUC = 0.959 values were obtained with the stacking ensemble learning method. BUN, MPV, Age, AST and Monocyte variables were determined as the most essential factors in the classification of coronary artery disease, respectively.
Coclusion: Stacking ensemble learning method provided the highest accuracy performance in the classification of coronary artery disease. Stacking ensemble learning method gives successful results in the classification of coronary artery diseases.

Anahtar Kelimeler

Coronary Artery Diseases, Stacking Ensemble Learning Methods, Artificial Intelligence, Meta Classifier

Proje Numarası

-

Kaynakça

• 1. Q.U.A. Mastoi, T.Y. Wah, R.G. Raj, U. Iqbal, “Automated Diagnosis of Coronary Artery Disease: A Review and Workflow”, Cardiology Research and Practice. Volume.2018, 2018, pp.1-9.
• 2. M.G. Tsipouras, T.P. Exarchos, D.I. Fotiadis, A.P. Kotsia, K.V. Vakalis, K.K. Naka, et al, “Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling”, IEEE Trans Inf Technol Biomed, Volume.12, No.4, 2008, pp.447-458.
• 3. E.J. Benjamin, P. Muntner, A. Alonso, M.S. Bittencourt, C.W. Callaway, A.P. Carson, et al, “Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association”, Circulation, Volume.139, No.10, 2019, pp.E56-E528.
• 4. R. Alizadehsani, M. Roshanzamir, M. Abdar, A. Beykikhoshk, A. Khosravi, M. Panahiazar, et al, “A database for using machine learning and data mining techniques for coronary artery disease diagnosis”, Scientific data, Volume.6, No.1, 2019, pp.1-13.
• 5. R. Alizadehsani, M. Abdar, M. Roshanzamir, A. Khosravi, P.M. Kebria, F. Khozeimeh, et al. “Machine learning-based coronary artery disease diagnosis: A comprehensive review”. Computers in Biology and Medicine, Volume.111, 2019. pp.1-14.
• 6. A.M. Poss, J.A. Maschek, J.E. Cox, B.J. Hauner, P.N. Hopkins, S.C. Hunt, et al, “Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease”, Journal of clinical investigation, Volume.130, No.3, 2020, pp.1363-1376.
• 7. L. Verma, S. Srivastava, P.C. Negi, “A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data”, Journal of medical systems. Volume.40, No.7, 178, 2016, pp.1-7.
• 8. Y. Zheng, M. Loziczonek, B. Georgescu, S.K. Zhou, F. Vega-Higuera, D. Comaniciu, Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes. In: Medical Imaging 2011, Image Processing. 2011.
• 9. S. Nikan, F. Gwadry-Sridhar, M. Bauer, Machine learning application to predict the risk of coronary artery atherosclerosis. In: Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016, pp.34-39.
• 10. U.R. Acharya, H. Fujita, O.S. Lih, M. Adam, J.H. Tan, C.K. Chua, “Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network”, Knowledge-Based Systems, Volume.132, 2017, pp.62-71.
• 11. E.M. Karabulut, T. İbrikçi, “Effective diagnosis of coronary artery disease using the rotation forest ensemble method”, Journal of medical systems. Volume.36, No.5, 2012, pp.3011-3018.
• 12. M. Amer, M. Goldstein, “Nearest-Neighbor and Clustering based Anomaly Detection Algorithms for RapidMiner”, Proc 3rd RapidMiner Community Meet Conferernce (RCOMM 2012). 2012.
• 13. Z.H. Zhou, Ensemble methods: Foundations and algorithms. Ensemble Methods: Foundations and Algorithms, CRC Press, 2012.
• 14. D.H. Wolpert, “Stacked generalization”, Neural Networks, Volume.5, No.2, 1992, pp.241-259.
• 15. L. Breiman, “Random forests”, Machine Learning”, Volume.45, No.1, 2001, pp.5-32.
• 16. Akar Ö, Güngör O. “Rastgele orman algoritması kullanılarak çok bantlı görüntülerin sınıflandırılması”, Jeodezi ve Jeoinformasyon Dergisi. Volume.146, 2012, pp.139-146. 17. K.J. Archer, R. V. “Kimes Empirical characterization of random forest variable importance measures”, Computer Statistics&Data Analysis, Volume.52, No.4, 2008, pp.2249-2260.
• 18. I. Rish, “An empirical study of the naive Bayes classifier”, In IJCAI 2001 workshop on empirical methods in artificial intelligence, Volume.3, No.22, 2001, pp.41-46.
• 19. P. Bhargavi, S. Jyothi, “Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils”, IJCSNS International journal of computer science and network security, Volume.9, No.8, 2009, pp.117-122.
• 20. K.P. Murphy, “Naive Bayes classifiers Generative classifiers”, Bernoulli. 2006.
• 21. J. Platt, Fast Training of Support Vector Machines using Sequential Minimal Optimization, In: Advances in Kernel Methods Support Vector Learning, MIT Press, 1999.
• 22. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, “Improvements to Platt’s SMO algorithm for SVM classifier design”, Neural computation, Volume.13, No.3, 2001, pp.637-649
• 23. D.P. Zipes, P. Libby, R.O. Bonow, D.L. Mann, G. F. Tomaselli, Braunwald's Heart Disease E-Book: A Textbook of Cardiovascular Medicine. Elsevier Health Sciences, 2018.
• 24. P. Pławiak, “Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system”, Expert Systems with Applications, Volume.92, 2018, pp.334-349.
• 25. R. Alizadehsani, M.H. Zangooei, M.J. Hosseini, J. Habibi, A. Khosravi, M. Roshanzamir, et al, “Coronary artery disease detection using computational intelligence methods, Knowledge-Based Systems, Volume.109, 2016, pp.187-197.
• 26. I. Babaoǧlu, O. Findik ,M. Bayrak, “Effects of principle component analysis on assessment of coronary artery diseases using support vector machine”, Expert Systems with Applications, Volume.37, No.3, 2010, pp.2182-2185.
• 27. D. Gola, J. Erdmann,B. Müller-Myhsok, H. Schunkert, I.R. König, “Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status”, Genetic Epidemiology, Volume.44, No.2, 2020, pp.125-138.
• 28. N.A. Setiawan, D.W. Prabowo, H.A. Nugroho, Benchmarking of feature selection techniques for coronary artery disease diagnosis. In: Proceedings - 2014 6th International Conference on Information Technology and Electrical Engineering: Leveraging Research and Technology Through University-Industry Collaboration, ICITEE 2014, 2014.
• 29. H. G. Arjenaki, M. H. N. Shahraki, N. Nourafza, “A low cost model for diagnosing coronary artery disease based on effective feature”, International Journal of Electronics Communication and Computer Engineering, Volume.6, No.1, 2015, pp.93-97.
• 30. R. Ani, A. Augustine, N.C. Akhil, O.S. Deepa “Random forest ensemble classifier to predict the coronary heart disease using risk factors”, In Proceedings of the International Conference on Soft Computing Systems, 2016, Springer, New Delhi. pp.701-710.
• 31. M.A. Jabbar, B.L. Deekshatulu, P. Chandra, Prediction of heart disease using random forest and feature subset selection. In: Innovations in bio-inspired computing and applications 2016, Springer, Cham, pp. 187-196.
• 32. Z. Masetic, A. Subasi. Congestive heart failure detection using random forest classifier. Computer methods and programs in biomedicine, Volume.130, 2016, pp.54-64.
• 33. Lo YT, Fujita H, Pai TW. Prediction of coronary artery disease based on ensemble learning approaches and co-expressed observations. Journal of Mechanics in Medicine and Biology, Volume.16, No.1 2016.