A Study on Performance Improvement of Heart Disease Prediction by Attribute Selection Methods

Abstract

Heart pumps blood for all tissues of the body. The deteriorate of this organ causes a severe illness, disability and death since cardiovascular diseases involve the diseases that related to heart and circulation system. Determination of the significance of factors affecting this disease is of great importance for early prevention and treatment of this disease. In this study, firstly, the best attributes set for Single Proton Emission Computed Tomography (SPECT) and Statlog Heart Disease (STATLOG) datasets were detected by using feature selection methods named RFECV (Recursive Feature Elimination with cross-validation) and SS (Stability Selection). Secondly, GBM (Gradient Boosted Machines), NB (Naive Bayes) and RF (Random Forest) algorithms were implemented with original datasets and with datasets having selected attributes by RFECV and SS methods and their performances were compared for each dataset. The experimental results showed that maximum performance increases were obtained on SPECT dataset by 14.81% when GBM algorithm was applied using attributes provided by RFECV method and on STATLOG dataset by 6.18% when GBM algorithm was applied using attributes provided by RFECV method. On the other hand, best accuracies were obtained by NB algorithm when applied using attributes of SPECT dataset provided by RFECV method and using attributes of STATLOG dataset provided by SS method. The results showed that medical decision support systems which can make more accurate predictions could be developed using enhanced machine learning methods by RFECV and SS methods and this can be helpful in selecting the treatment method for the experts in the field.

Keywords

• Cardiovascular disease,attribute importance,attribute selection,stability selection,recursive feature selection

Öznitelik Seçimi Algoritmaları Kullanılarak Kalp Hastalığı Tahmininin Performans İyileştirmesi Üzerine Bir Çalışma

Abstract

Keywords

• Kardiyovasküler hastalık,özniteliğin önemi,öznitelik seçimi,kararlılık seçimi,özyinelemeli özellik seçimi

References

1. [1]. R. Das, I. Turkoglu, A. Sengur, “Effective Diagnosis of Heart Disease through Neural Network Ensemble”, Expert Syst Appl, vol. 36, no 4, pp. 7675- 7680, 2009.
2. [2]. Coronary heart disease. Available online: https://www.bhf.org.uk/heart-health/conditions/coronary-heart-disease (accessed on 01 July 2018).
3. [3]. J.S. Sonawane, D.R. Patil, V.S. Thakare, “Survey on Decision Support System for Heart Disease”, International Journal of Advancements in Technology, vol. 4, no 1, pp. 89-96, 2013.
4. [4]. M. Ciecholewski “Support Vector Machine Approach to Cardiac SPECT Diagnosis”, In: J.K. Aggarwal, R.P. Barneva, V.E. Brimkov, K.N. Koroutchev, E.R. Korutcheva (eds) Combinatorial Image Analysis. IWCIA 2011. Lecture Notes in Computer Science, vol. 6636, pp. 432-443, 2011.
5. [5]. O.O. Ebenezer, K.O. Oyebade, A. Khashman, “Heart Diseases Diagnosis Using Neural Networks Arbitration”, J. Intelligent Systems and Applications, vol. 7, no 12, pp. 72-79, 2015.
6. [6]. H. Yang, J.M. Garibaldi, “A hybrid model for automatic identification of risk factors for heart disease”, J Biomed Inform, vol. 58, pp. 171-82, 2015.
7. [7]. L.A. Kurgan, K.J. Cios, R. Tadeusiewicz, M. Ogiela, L.S. Goodenday, “Knowledge discovery approach to automated cardiac SPECT diagnosis”, Artif Intell Med, vol. 23, no 2, pp.149-169, 2001.
8. [8]. J. Padmavathi, L. Heena, S. Fathima “Effectiveness of Support Vector Machines in Medical Data mining”, Journal of Communications Software and Systems, vol. 11, no 1, pp.25-30, 2015.

9. [9]. S. El Rafaie, M.S. Abdel-Badeeh, K. Revett, “On the use of SPECT imaging datasets for automated classification of ventricular heart disease”, Informatics and Systems, 8th International Conference on Cairo, Egypt, pp. 14-16 May 2012.
10. [10]. Y. Prasad, K.K. Biswas, “PSO - SVM Based Classifiers: A Comparative Approach”, In: Ranka S. et al. (eds) Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol. 94, pp. 241-252, 2010.
11. [11]. K.,Vanisree, J. Singaraju, “Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks”, International Journal of Computer Applications, vol. 19, no 6, pp.6-12, 2011.
12. [12]. R.N. MadhuSudana, K. Kannan, M. Manisha, S.R. Diptendu, “Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization”, J Healthc Eng, vol. 5907264, pp. 1-27, 2017.
13. [13]. S. Sivagowry, “Feature Diminution by Using Particle Swarm Optimization for Envisaging the Heart Syndrome”, International Journal of Information Technology and Computer Science, vol. 2, pp. 35-43, 2015.
14. [14]. N.A. Setiawan, “Rule Selection for Coronary Artery Disease Diagnosis Based on Rough Set”, International Journal of Recent Trends in Engineering, vol. 2, no 5, pp. 198-202, 2009.
15. [15]. N.A. Setiawan, “Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory”, International Journal of Rough Sets and Data Analysis, vol. 1, no 1, pp. 65-80, 2014.
16. [16]. D. Raghu, T. Srikanth, R. Jacub, “Probability Based Heart Disease Prediction using Data Mining Technique”, International Journal of Computer Science and Technology, vol. 2, no 4, pp. 66-68, 2011.
17. [17]. J. Vijayashree, N.C.S. NarayanaIyengar, “Heart Disease Prediction System Using Data Mining and Hybrid Intelligent Techniques: A Review”, International Journal of Bio-Science and Bio-Technology, vol. 8, no 4, pp.139-148, 2016.
18. [18]. Spect dataset. Available online: https://archive.ics.uci.edu/ml/datasets/spect+heart (accessed on 01 June 2018)
19. [19]. Statlog dataset. Available online: http://archive.ics.uci.edu/ml/datasets/Statlog+%28Heart%29 (accessed on 01 June 2018)
20. [20]. P. Ivens, A. Paulo, C. Donald, “The use of machine learning algorithms in recommender systems: A systematic review”, Expert Syst Appl, vol. 97, pp. 205-227, 2018.
21. [21]. J. Cai, J. Luo, S. Wang, S. Yang, “Feature selection in machine learning: a new perspective. Neurocomputing”, vol. 300, pp. 70-79, 2018.
22. [22]. L. Huan, H. Motoda, “Computational Methods of Feature Selection”, Chapman & Hall/Crc Data Mining and Knowledge Discovery Series, 2007.
23. [23]. H. Liu, H. Motoda, R. Setiono, Z. Zhao, “Feature Selection: An Everlasting Frontier in Data Mining”, Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, pp. 4-13, 2010.
24. [24]. P. Zhou, X. Hu, P. Li, X. Wu, “Online feature selection for high-dimensional class-imbalanced data”, Knowledge-Based Systems, vol. 136, pp. 187–199, 2017.
25. [25]. A. Filali, C. Jlassi, N. Arous, “Recursive Feature Elimination with Ensemble Learning Using SOM Variants”, International Journal of Computational Intelligence and Applications, vol. 16, no 1, pp.1-25, 2017.
26. [26]. N. Meinshausen, P. Bühlmann, “Stability selection”, J. R. Statist Soc. B, vol. 72, no. 4, pp.417–473, 2010.
27. [27]. J.H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine”, The Annals of Statistics, vol. 29, no 4, pp.1189-1232, 2001.
28. [28]. J. Han, M. Kamber, J. Pei, Data Mining Concepts and Techniques, 3rd ed, Waltham, USA: Elsevier, 2012.
29. [29]. L. Breiman, “Random forests”, Mach Learn, vol. 45, no 1, pp.5-32, 2011.
30. [30]. S.A. Shaikh, “Measures derived from a 2x2 table for an accuracy of a diagnostic test”, J Biom Biostat, vol.2, no 128, pp.1-4, 2011.