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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.10, No.11, Nov. 2018

Medical Big Data Classification Using a Combination of Random Forest Classifier and K-Means Clustering

Full Text (PDF, 391KB), PP.11-19


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Author(s)

R. Saravana kumar, P. Manikandan

Index Terms

Decision trees;k-means clustering;medical big data;random forest;Classification

Abstract

An efficient classification algorithm used recently in many big data applications is the Random forest classifier algorithm.  Large complex data include patient record, medicine details, and staff data etc., comprises the medical big data. Such massive data is not easy to be classified and handled in an efficient manner. Because of less accuracy and there is a chance of data deletion and also data missing using traditional methods such as Linear Classifier K-Nearest Neighbor, Random Clustering K-Nearest Neighbor. Hence we adapt the Random Forest Classification using K-means clustering algorithm to overcome the complexity and accuracy issue. In this paper, at first the medical big data is partitioned into various clusters by utilizing k- means algorithm based upon some dimension. Then each cluster is classified by utilizing random forest classifier algorithm then it generating decision tree and it is classified based upon the specified criteria. When compared to the existing systems, the experimental results indicate that the proposed algorithm increases the data accuracy.

Cite This Paper

R. Saravana kumar, P. Manikandan, "Medical Big Data Classification Using a Combination of Random Forest Classifier and K-Means Clustering", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.11, pp.11-19, 2018. DOI: 10.5815/ijisa.2018.11.02

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