Dual Edge Classifier Based Support Vector Machine (Desvm) Classifier for Clinical Dataset
S. Kavipriya1, T. Deepa2
1S. Kavipriya, Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, (Tamil, Nadu), India.
2Dr. T. Deepa, Assistant Professor, Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, (Tamil Nadu), India.
Manuscript received on 13 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 331-338 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2229037619/19©BEIESP
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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: Data mining is the progression of determining hidden information that are available in the existing data. Data mining discovers interesting, convenient relationships in huge volume of data. Many fields including medical field is using data mining for classifying the data. Classification is method which assigns a data in the collection to predict the objective class. Classifying a diabetic patient is tedious job in the current medical field. The main intention of this paper is to propose a novel classifier enhancing support vector machine to correctly classify the diabetic patients more accurately than the previous classifiers. Performance metrics such as sensitivity, specificity, rate of true positive and false positive, precision, accuracy and time taken for feature selection are used. In the proposed classifier threshold value is fixed for metric recall and true negative rate. The results are demonstrated with better performance.
Keywords: Classification, SVM, Gestational Diabetes, Prediction, and Accuracy.
Scope of the Article: Classification