Gestational Diabetes Diagnosis with MSVM, MJ48 Classifier Models
S. Saradha1, P. Sujatha2
1Dr. S. Saradha, Assistant Professor, Department of Computer Science, VISTAS India.
2Dr. P. Sujatha, Professor, Department of BCA & IT, VISTAS India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 244-248 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10400982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1040.0982S1119
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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: This paper focuses on designing an automated system for diagnosing gestational diabetes. Classification is one of the common predictive data mining tasks. It arranges the information and assembles a model to deliver the new grouped information. ‘Gestational diabetes mellitus’ (GDM) is a form of diabetes that occurs during pregnancy due to hormonal changes. Pregnant Women with GDM are at highest threat of future diabetes, especially type-2 diabetes. To diagnose the GDM, the two classifier models are proposed such as .Modified Support Vector Machine (MSVM) and Modified J48 (MJ48). Based on the performance analysis, the classifier model MJ48 provides more accuracy and less error rate than MSVM proposed classifier model.
Keywords: Data Mining, Classifiers, GDM, OGTT, MSVM, MJ48, Accuracy.
Scope of the Article: Design and Diagnosis