A Hybrid Clustering Data Mining Technique (HCDMT) for Predicting SLE
A. Malarvizhi1, S. Ravichandran2
1Ms. A. Malarvizhi, Research Scholar, Assistant Professor, PG and Research, Department of Computer Science, H.H. The Rajah’s College (A), Pudukkottai (Tamil Nadu), India.
2Mr. S. Ravichandran, Assistant Professor and Head, PG and Research Department of Computer Science, H.H. The Rajah’s College (A), Pudukkottai (Tamil Nadu), India.
Manuscript received on 08 September 2019 | Revised Manuscript received on 17 September 2019 | Manuscript Published on 26 October 2019 | PP: 512-515 | Volume-8 Issue-11S2 September 2019 | Retrieval Number: K108809811S219/2019©BEIESP | DOI: 10.35940/ijitee.K1088.09811S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: SLE is an auto immune and complex disease. Predicting Systemic Lupus Erythematosus (SLE) is significantly challenging due to its high level of heterogeneity in symptoms. There is a limitation on the tools used for predicting SLE accurately. This paper proposes a machine learning approach to predict the disease from SLE data set and classify patients in whom the disease is active. The data purified and selected for classification improves the accuracy of the proposed method called HCDMT (Hybrid Clustering Data Mining Technique), an amalgamation of CART and k-Means, was evaluated on SLE data. It was found to predict above 95% of SLE cases.
Keywords: SLE, Clustering, Mining, Machine Learning.
Scope of the Article: Data Mining