Three-Fold Integrated Clutsering-Classification (TICC) Strategy for Diabetes Mellitus Prediction

Authors(2) :-Dr. V. Saravanan, Monika Seles

MLT finds potentially useful patterns in the data. Three MLT deployed for the diabetes mellitus prediction is presented subsequently with a brief on proposed method, experimental set up, test results and performance comparison. The proposed classifiers are tested with the original dataset. The results are recorded first. Subsequently the dataset will be subject to cluster and the this will be the first fold of the proposed technique. In the expansion step the assigned cluster will be a separate instance in the dataset. This will be the second fold of the proposed technique. Classification will be deployed as the third fold of the proposed technique. This proposed three fold integrated clustering-classification technique for diabetes mellitus prediction significantly improves the performance of the diabetes mellitus prediction. After the proposed strategy is carried out, results are recorded and compared.

Authors and Affiliations

Dr. V. Saravanan
Associate Professor & Head, PG and Research Department of Information Technology, Hindusthan College of Arts and Science, Coimbatore, Tamil Nadu, India
Monika Seles
M. Phil Research Scholar, Hindusthan College of Arts and Science, Coimbatore, Tamil Nadu, India

MLT, Diabetes Mellitus, Classification, Clustering

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 700-708
Manuscript Number : CSEIT1724165
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. V. Saravanan, Monika Seles, "Three-Fold Integrated Clutsering-Classification (TICC) Strategy for Diabetes Mellitus Prediction", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.700-708, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT1724165

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