R. Saravana kumar

Work place: Department of computer science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore

E-mail: saravanaram0516@gmail.com


Research Interests: Computational Science and Engineering, Data Structures and Algorithms


R.Saravana kumar ( Ramachandran) obtained B.E Degree in computer science and Engineering from Bharathiyar University, Coimbatore, tamilnadu, india in 2003. He obtained M.E degree in computer sciences and Engineering from Anna University, Chennai in 2007 .He has done PhD in Data Science from Anna University, Chennai in the year of 2015. Currently he is working as a professor in computer science and Engineering department from Dayananda Sagar Academy of Technology and Management, Bangalore, India. His area of Interest is Data Science.

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

By R. Saravana kumar P. Manikandan

DOI: https://doi.org/10.5815/ijisa.2018.11.02, Pub. Date: 8 Nov. 2018

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.

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