References
- R. C. Chen, C. Dewi, S. W. Huang, and R. E. Caraka, Selecting critical features for data classification based on machine learning methods, J Big Data 7 (2020), no. 52.
- UCI, Chronic kidney disease data set. Available at: https://archive.ics.uci.edu/ml/datasets/chronic_kidney_disease [last accessed February 2020]
- Z. M. Hira and D. F. Gillies, A review of feature selection and feature extraction methods applied on microarray data, Adv. Bioinform. 2015 (2015), 198363.
- S. Rousseau and R. Rousseau, Data envelopment analysis as a tool for constructing scientometric indicators, Scientometrics. 40 (1997), 45-56. https://doi.org/10.1007/BF02459261
- N. Cui, J. Hu, and F. Liang, Complementary dimension reduction, Stat. Anal. Data. Min. 14 (2020), 1-10.
- B. M. Konopka, F. Lwow, M. Owczarz, and L. Laczmanski, Exploratory data analysis of a clinical study group: Development of a procedure for exploring multidimensional data, PLOS One 13 (2018), e0201950.
- S. Friedrich, G. Antes, S. Behr, H. Binder, W. Brannath, F. Dumpert, K. Ickstadt, H. A. Kestler, J. Lederer, H. Leitgob, M. Pauly, A. Steland, A. Wilhelm, and T. Friede, Is there a role for statistics in artificial intelligence? Adv. Data Anal. Classif. (2021). https://doi.org/10.1007/s11634-021-00455-6
- A. Onan and S. Korukoglu, A feature selection model based on genetic rank aggregation for text sentiment classification, J. Inf. Sci. 43 (2017), 25-38. https://doi.org/10.1177/0165551515613226
- A. Onan, A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer, Expert Syst. Appl. 42 (2015), 6844-6852. https://doi.org/10.1016/j.eswa.2015.05.006
- A. Onan, Classifier and feature set ensembles for web page classification, J. Inf. Sci. 42 (2016), 150-165. https://doi.org/10.1177/0165551515591724
- A. Onan, Biomedical text categorization based on ensemble pruning and optimized topic modelling, Comp. Math. Methods Med. 2018 (2018), 2497471.
- M. Rostami, K. Berahmand, E. Nasiri, and S. Forouzande, Review of swarm intelligence-based feature selection methods, Eng. Appl. Artif. Intel. 100 (2021). https://doi.org/10.1016/j.engappai.2021.104210
- D. Mishra and S. Sharma, Performance analysis of dimensionality reduction techniques: A comprehensive review, in Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering, Springer, Singapore, 2021, pp. 639-651.
- R. Aziz, C. K. Verma, and N. Srivastava, Dimension reduction methods for microarray data: A review, AIMS Bioeng. 4 (2017), 179-197. https://doi.org/10.3934/bioeng.2017.1.179
- R. Aziz, C. K. Verma, and N. Srivastava, A novel approach for dimension reduction of microarray, Comput Biol. Chem. 71 (2017), 161-169. https://doi.org/10.1016/j.compbiolchem.2017.10.009
- R. A. Musheer, C. Verma, and N. Srivastava, Novel machine learning approach for classification of highdimensional microarray data, Soft Comput. 23 (2019), 13409-13421. https://doi.org/10.1007/s00500-019-03879-7
- R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction, J. Appl. Sci. Technol. Trends. 1 (2020), 56-70. https://doi.org/10.38094/jastt1224
- W. Young, G. Weckman, and W. Holland, A survey of methodologies for the treatment of missing values within datasets: Limitations and benefits, Theor. Issues Ergon. Sci. 12 (2011), 15-43. https://doi.org/10.1080/14639220903470205
- J. Tang, Z. Chen, A. W. Fu, and D. W. Cheung, Capabilities of outlier detection schemes in large datasets, framework and methodologies, Knowl. Inf. Syst. 11 (2006), 45-84. https://doi.org/10.1007/s10115-005-0233-6
- S. Thudumu, P. Branch, J. Jin, and J. Singh, A comprehensive survey of anomaly detection techniques for high dimensional big data, J. Big Data 7 (2020).
- X. Xu, H. Liu, L. Li, and M. Yao, A comparison of outlier detection techniques for high-dimensional data, Int. J. Comp. Intell. Syst. 11 (2018), 652-662. https://doi.org/10.2991/ijcis.11.1.50
- S. Tshering, T. Okazaki, and S. Endo, A method to identify missing data mechanism in incomplete dataset, Int. J. Comput. Sci. Network Sec. 13 (2013).
- Y. A. Ozcan and K. Tone, Health care benchmarking and performance evaluation: An assessment using data envelopment analysis (DEA), 2nd ed., Springer, New York, NY, USA, 2014.
- E. Thanassoulis, K. D. Witte, J. Johnes, G. Johnes, G. Karagianni, and C. S. Portela, Applications of data envelopment analysis in education, In Data envelopment analysis. International series in operations research & management science, Vol. 238, Springer, Boston, MA, USA, 2016.
- D. Jain and V. Singh, Feature selection and classification systems for chronic disease prediction: A review, Egypt Inform. J. 19 (2018), 179-189. https://doi.org/10.1016/j.eij.2018.03.002
- X. Wang, Y. Yan, and X. Ma, Feature selection method based on differential correlation information entropy, Neural Process Lett. 52 (2020), 1339-1358. https://doi.org/10.1007/s11063-020-10307-7
- M. S. Wibawa, I. M. D. Maysanjaya, and I. M. A. W. Putra, Boosted classifier and features selection for enhancing chronic kidney disease diagnose, (International Conference on Cyber and IT Service Management, Denpasar, Indonesia), Aug. 2017. https://doi.org/10.1109/CITSM.2017.8089245
- D. Grissa, M. Petera, M. Brandolini, A. Napoli, B. Comte, and E. P. Guillot, Feature selection methods for early predictive biomarker discovery using untargeted metabolomic data, Front. Mol. Biosci. 3 (2016). https://doi.org/10.3389/fmolb.2016.00030
- J. Qin, L. Chen, Y. Liu, C. Liu, C. Feng, and B. Chen, A machine learning methodology for diagnosing chronic kidney disease, IEEE Access. 8 (2020), 20991-21002. https://doi.org/10.1109/ACCESS.2019.2963053
- M. Esfandiari and M. Rizvandi, An application of TOPSIS method for ranking different strategic planning methodology, Manag. Sci. Lett. 4 (2014), 1445-1448. https://doi.org/10.5267/j.msl.2014.6.022
- K DIGO, KDIGO clinical practice guideline on the ecaluation and management of candidates for kidney transplantation, 2018.Available at: https://kdigo.org/wp-content/uploads/2018/08/KDIGO-Txp-Candidate-GL-Public-Review-Draft-Oct-22.pdf
- N. Saravanan, G. Sathish, and J. M. Balajee, Data wrangling and data leakage in machine learning for healthcare, J. Emerg. Technol. Innov. Res. 5 (2018), https://www.jetir.org/papers/JETIRC006413.pdf
- F. Farias, T. Ludermir, and C. B. Filho, Similarity based stratified splitting: an approach to train better classifiers, arXiv preprint, 2020. https://doi.org/10.48550/arXiv.2010.06099
- J. Tanha, Y. Abdi, N. Samadi, N. Razzaghi, and M. Asadpour, Boosting methods for multi-class imbalanced data classification: an experimental review, J. Big Data 7 (2020). https://doi.org/10.1186/s40537-020-00349-y
- M. Bader-El-Den, E. Teitei, and T. Perry, Biased random forest for dealing with the class imbalance problem, IEEE Trans. Neural Netw. Learn. Syst. 30 (2019), 2163-2172. https://doi.org/10.1109/TNNLS.2018.2878400