Abstract
The objective of this study had reduced the identification time and to get best diagnosis model in Chronic Kidney Disease (CKD). The target was development of the strong and computationally proficient model for classification of CKD. This work had used four classification models like Naive Bayes, Multilayer Perceptron, OneR, Classification and Regression Tree (CART) to classify the CKD data set and compared the classification accuracy with all classifier. Similarly Feature Selection Technique (FST), ranking based namely Chi Squared AttributeEval (CSAE), One-R AttributeEval (ORAE) and Search based namely Genetic Search-J48 (GS-J48), Genetic Search-CART (GS-CART) have used. The contribution of this research work is to recognize and classify the CKD problem and propose a new FST namely Genetic Search Intersection Based Feature Selection Technique (GS-IBFST). The classifier offers higher accuracy with GS-IBFST compare to without FSTs and existing FSTs.
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References
Subasi, A., Alickovic, E., Kevric, J.: Diagnosis of chronic kidney disease by using random forest. In: 2017 Proceedings of the International Conference on Medical and Biological Engineering, pp. 589–594 (2017)
Polat, H., Danaei Mehr, H., Cetin, A.: Diagnosis of chronic kidney disease based on support vector machine by feature selection methods. J. Med. Syst. 41(4), 55 (2017)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2012)
Pujari, A.: Data Mining Techniques. University Press, Hyderabad (2013)
Jantawan, B., Tsai, C.: A comparison of filter and wrapper approaches with data mining techniques for. Int. J. Innov. Res. Comput. Commun. Eng. 2, 4501–4508 (2014)
Yildirim, P.: Filter based feature selection methods for prediction of risks in hepatitis disease. Int. J. Mach. Learn. Comput. 5, 258–263 (2015)
Hall, M.: Correlation-based feature selection for machine learning. Methodology 21i195-i20, pp. 1–5 (1999)
Liu, H., Setiono, R., Science, C., Ridge, K.: Chi2: Feature Selection, pp. 388–391 (1995)
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11, 63–91 (1993)
Shrivas, A.K., Sahu, S.K., Hota, H.S.: Classification of chronic kidney disease with proposed union based feature selection technique, pp. 503–507 (2018)
Charleonnan, A., et al.: Predictive analytics for chronic kidney disease using machine learning techniques. In: 2016 Management and Innovation Technology International Conference, pp. MIT-80–MIT-83 (2016). https://doi.org/10.1109/miticon.2016.8025242
Kunwar, V., Chandel, K., Sabitha, A.S., Bansal, A.: Chronic kidney disease analysis using data mining classification. In: 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 300–305. IEEE (2016). https://doi.org/10.1109/confluence.2016.7508132
Boukenze, B., Haqiq, A., Mousannif, H.: Predicting chronic kidney failure disease using data mining techniques, vol. 397 (2017)
Russell, S., Norvig, P.: Artificial Intelligence A Modern Approach. Series in Artificial Intelligence. Prentice Hall, Upper Saddle River (2003)
Haykin, S.: Neural Networks and Learning Machines. Pearson Prentice Hall, Upper Saddle River (2008). 936 pLinks 3
Nasa, C., Suman, S.: Evaluation of different classification techniques for WEB data. Int. J. Comput. Appl. 52, 34–40 (2012)
Alam, F., Pachauri, S.: Comparative study of J48, Naive Bayes and One-R classification technique for credit card fraud detection using WEKA. Adv. Comput. Sci. Technol. 10, 1731–1743 (2017)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation (1985)
Sivanandam, S.N., Deepa, S.N.: Principles of Soft Computing. Wiley, Hoboken (2014)
Novakovic, J., Strbac, P., Bulatovic, D.: Toward optimal feature selection using ranking methods and classification algorithms. Yugosl. J. Oper. Res. 21, 119–135 (2011)
Karegowda, A.G., Manjunath, A., Jayaram, M.: Application of genetic algorithm optimized neural network connection weights for medical diagnosis of PIMA Indians diabetes. Int. J. Soft Comput. 2, 15–23 (2011)
Ashraf, M., Chetty, G., Tran, D.: Feature selection techniques on thyroid, hepatitis, and breast cancer datasets. Int. J. Data Min. Intell. Inf. Technol. Appl. 3, 1–8 (2013)
Arun Kumar, C., Sooraj, M.P., Ramakrishnan, S.: A comparative performance evaluation of supervised feature selection algorithms on microarray datasets. Procedia Comput. Sci. 115, 209–217 (2017)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining. Morgan Kaufmann series in data management systems (2011). https://doi.org/10.1002/1521-3773(20010316)40:6%3c9823::aid-anie9823%3e3.3.co;2-c
UCI Machine Learning Repository of machine learning databases (2015). https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease. Accessed 1 Jan 2016
Machine Learning Group at the University of Waikato. https://www.cs.waikato.ac.nz/ml/weka/downloading.html
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Sahu, S.K., Chandrakar, P.K. (2020). Classification of Chronic Kidney Disease with Genetic Search Intersection Based Feature Selection Technique. In: Nain, N., Vipparthi, S. (eds) 4th International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2019. ICIoTCT 2019. Advances in Intelligent Systems and Computing, vol 1122. Springer, Cham. https://doi.org/10.1007/978-3-030-39875-0_2
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