Efficient Datamining Model for Prediction of Chronic Kidney Disease using Wrapper Methods
Shiva Prasad S1, Ramaswamyreddy A2, Dinesh K3, Veeraiah D4

1Shiva Prasad S, VFSTR University, Guntur (Andhra Pradesh), India.
2Ramaswamyreddy A, Mallareddy Institute of Technology, Secunderabad (Telangana), India.
3Dinesh K, VFSTR University, Guntur (Andhra Pradesh), India.
4Veeraiah D, VFSTR University, Guntur (Andhra Pradesh), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 141-146 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10270275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the present generation, majority of the people are highly affected by kidney diseases. Among them, chronic kidney is the most common life threatening disease which can be prevented by early detection. Histological grade in chronic kidney disease provides clinically important prognostic information. Therefore, machine learning techniques are applied on the information collected from previously diagnosed patients in order to discover the knowledge and patterns for making precise predictions. A large number of features exist in the raw data in which some may cause low information and error; hence feature selection techniques can be used to retrieve useful subset of features and to improve the computation performance. In this manuscript we use a set of Filter, Wrapper methods followed by Bagging and Boosting models with parameter tuning technique to classify chronic kidney disease. Capability of Bagging and Boosting classifiers are compared and the best ensemble classifier which attains high stability with better promising results is identified.
Keywords: Bagging; Boosting; Chronic Kidney; Filter Methods; Wrapper Methods.
Scope of the Article: Regression and Prediction