Performance Study Of Uncertainty Based Feature Selection Method On Detection Of Chronic Kidney Disease With SVM Classification

Lailly Syifa'ul Qolby, Joko Lianto Buliali, Ahmad Saikhu

Abstract


Chronic Kidney Disease (CKD) is a disorder that impairs kidney function. Early signs of CKD patients are very difficult until they lose 25% of their kidney function. Therefore, early detection and effective treatment are needed to reduce the mortality rate of CKD sufferers. In this study, the authors diagnose the CKD dataset using the Support Vector Machine (SVM) classification method to obtain accurate diagnostic results. The authors propose a comparison of the result on applying the feature selec- tion method to get the best feature candidates in improving the classification result. The testing process compares the Symmetrical Uncertainty (SU) and Multivariate Symmetrical Uncertainty (MSU) feature selection method and the SVM method as a classification method. Several experimental scenarios were carried out using the SU and MSU feature selection methods using the CKD dataset. From the results of the tests carried out, it shows that using the MSU feature selection method with 80%: 20% data split produces nine important features with an accuracy value of 0.9, sensi- tivity 0.84, specification 1.0, and when viewed on the ROC graph, the MSU method graph shows the true positive value is higher than the false positive value. So the classification using the MSU feature selection method is better than the SU feature selection method by 90% accuracy

Keywords


Chronic Kidney Disease; Feature Selection; Support Vector Machine; Uncertainty

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References


Sosa-Cabrera G, García-Torres M, Gomez-Guerrero S, Schaerer CE, Divina F. A Multivariate Approach to The Symmetrical Uncertainty Measure: Application to Feature Selection Problem. Information Sciences 2019 August;494:1–20. https://doi.org/10.1016/j.ins.2019.04.046.

Polat H, Mehr HD, Cetin A. Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods. Journal of Medical Systems 2017 February;41(4):1–11. https://doi.org/10.1007/s10916-017-0703-x.

Kumar CS, Thangaraju P. Improving Classifer Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines. International Journal of Engineering and Advanced Technology 2019 August;8(6):3697–3706. https://doi.org/10.35940/ijeat.f9377.088619.

Piao M, Piao Y, Lee J. Symmetrical Uncertainty-Based Feature Subset Generation and Ensemble Learning for Electricity Customer Classifcation. Symmetry 2019 April;11(4):498–508. https://doi.org/10.3390/sym11040498.

Akmam EF, Siswantining T, Soemartojo SM, Sarwinda D. Multiple Imputation with Predictive Mean Matching Method for Numerical Missing Data. In: Proceedings of The 3rd International Conference on Informatics and Computational Sciences (ICICoS) IEEE; 2019. p. 1–6. https://doi.org/10.1109/icicos48119.2019.8982510.

Bertsimas D, Orfanoudaki A, Pawlowski C. Imputation of clinical covariates in time series. Journal of Machine Learning Research 2020 November;110(1):185–248. https://doi.org/10.1007/s10994-020-05923-2.

Abidin NZ, Ritahani A, A N. Performance analysis of Machine Learning Algorithms for Missing Value Imputation. International Journal of Advanced Computer Science Applications 2018;9(6):442–447. https://doi.org/10.14569/ijacsa.2018.090660.

Hartini E. Classifcation of Missing Values Handling Method During Data Mining: A Review. Sigma Epsilon 2017 February;19(1):11–18. https://doi.org/10.17146/tdm.2017.19.1.3159.

Tsai CF, Chen YC. The Optimal Combination of Feature Selection and Data Discretization: An Empirical Study. Information Sciences 2019 dec;505:282–293. https://doi.org/10.1016/j.ins.2019.07.091.

Remeseiro B, Bolon-Canedo V. A Review of Feature Selection Methods in Medical Applications. Computers in Biology and Medicine 2019 September;112:103375. https://doi.org/10.1016/j.compbiomed.2019.103375.

Moayedikia A, Ong KL, Boo YL, Yeoh WG, Jensen R. Feature Selection for High Dimensional Imbalanced Class Data Using Harmony Search. Engineering Applications of Artifcial Intelligence 2017 January;57:38–49. https://doi.org/10.1016/j.engappai.2016.10.008.

Arias-Michel R, Garcia-Torres M, Schaerer C, Divina F. Feature Selection Using Approximate Multivariate Markov Blankets. In: Lecture Notes in Artifcial Intelligence and Lecture Notes in Bioinformatics Springer International Publishing; 2016.p. 114–125. https://doi.org/10.1007/978-3-319-32034-2_10.

Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyaf J, et al. Neural Network and Support Vector Machine for the Prediction of Chronic Kidney Disease: A Comparative Study. Computers in Biology Medicine 2019 jun;109:101–111. https://doi.org/10.1016/j.compbiomed.2019.04.017.

Piao Y, Ryu KH. A Hybrid Feature Selection Method Based on Symmetrical Uncertainty and Support Vector Machine for High-Dimensional Data Classifcation. In: Artifcial Intelligence and Lecture Notes in Bioinformatics Springer International Publishing; 2017.p. 721–727. https://doi.org/10.1007/978-3-319-54472-4_67.

Amirgaliyev Y, Shamiluulu S, Serek A. Analysis of Chronic Kidney Disease Dataset by Applying Machine Learning Methods. In: Proceedings of The 12th International Conference on Application of Information and Communication Technologies (AICT) IEEE; 2018. p. 120–123. https://doi.org/10.1109/icaict.2018.8747140.

Srivastava DK, Bhambhu L. Data Classifcation Using Support Vector Machine. Journal of Theoretical and Applied Information Technology 2010;12(1):1–7.

V RB, Sriraam N, Geetha M. Classifcation of Non-Chronic and Chronic Kidney Disease Using SVM Neural Networks. International Journal of Engineering Technology 2017 dec;7(1.3):191–194. https://doi.org/10.14419/ijet.v7i1.3.10669




DOI: http://dx.doi.org/10.12962/j20882033.v32i2.10483

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