Support Vector Machine – A Large Margin Classifier to Diagnose Skin Illnesses. (2016)

• Record Type: Journal Article
• Authors: Parikh, Krupal S.; Shah, Trupti P.
• Is Part Of: Procedia technology. Volume 23(2016)
• Page Start: 369
• Page End: 375
• Publication Date: 2016
• Subjects: Support Vector Machine -- Accuracy -- F-Score -- G-Score; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.03.039
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: Support Vector Machine (SVM) have been very popular as a large margin classifier due its robust mathematical theory. It has many practical applications in a number of fields such as in bioinformatics, in medical science for diagnosis of diseases, in various engineering applications for prediction of model, in finance for forecasting etc. It is widely used in medical science because of its powerful learning ability in classification. It can classify highly nonlinear data using kernel function. This paper proposes and analyses diagnostic model to classify the most common skin illnesses and also provide a useful insight into the SVM algorithm. In rural areas where people are generally treated by paramedical staff, skin patients are not subject to proper diagnosis resulting in mistreatment. We think SVM is a good tool for proper diagnosis. This paper uses various kernels for classification and achieving the best accuracy of 95.39%.