Performance evaluation of random forest with feature selection methods in prediction of diabetes

Title

Performance evaluation of random forest with feature selection methods in prediction of diabetes

Creator

Raghavendra S.; Santosh Kumar J.

Description

Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved.

Source

International Journal of Electrical and Computer Engineering, Vol-10, No. 1, pp. 353-359.

Date

2020-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Classification accuracy; Data mining; Feature selection method; Percentage split; Random forest

Coverage

Raghavendra S., Department of Computer Science and Engineering, CHRIST Deemed To Be University, Kanmanike, Kumbalgodu, Mysore Road, Bangalore, 560074, India; Santosh Kumar J., Department of Computer Science and Engineering, KSSEM, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 20888708

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijece.v10i1.pp353-359

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073363164&doi=10.11591%2fijece.v10i1.pp353-359&partnerID=40&md5=bbbe979964eaf91d3b7e7eded475f799