Title: Hybrid data mining model for the classification and prediction of medical datasets

Authors: S. Raghavendra; M. Indiramma

Addresses: Computer Science and Engineering Research Centre, BMS College of Engineering, Bull Temple Road, Bangalore-19, Karnataka, India ' Department of Computer Science and Engineering, BMS College of Engineering, Bull Temple Road, Bangalore-19, Karnataka, India

Abstract: Prediction and classification of medical datasets help in reducing the number of diagnostic procedure to identify the diseases there by providing economical solutions for healthcare systems and medical diagnosis software system. Data pre-processing plays a vital role in preparing effective and efficient data for data mining. Feature selection helps in providing important attributes for building comprehensive predictive models. In many existing work on classification and prediction of medical datasets, either full set of attributes or reduced set of attributes are used to find the classification accuracy. But the classification accuracy achieved from these is not satisfactory. Hence, in this paper we propose hybrid data mining predictive model for attribute selection based on entropy evaluation, mean evaluation, and threshold evaluation. In this prediction model we use feature subset selection methods (FSM) like forward selection and backward elimination method to select the best subset of attributes to reduce training time and to improve performance of the system.

Keywords: classification accuracy; entropy evaluation; EE; feature subset selection methods; FSM; mean evaluation; ME; medical datasets; threshold evaluation; TE.

DOI: 10.1504/IJKESDP.2016.084603

International Journal of Knowledge Engineering and Soft Data Paradigms, 2016 Vol.5 No.3/4, pp.262 - 284

Received: 07 Jun 2016
Accepted: 22 Dec 2016
Published online: 17 Jun 2017 *

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