Support Vector Machines

Support Vector Machine (SVM) is a recognized, supervised machine learning technique for classification and other learning activities. Based on statistical theory, it was conceptualised by Cortes and Vapnik in 1995 and modified by Vapnik in 1998. It functions as a discriminative classifier, a linear separator of two data points with the intent to recognise two separate classes in a multidimensional setting. It generates an optimal hyperplane, which classifies new examples. SVM spots the prime separating hyperplane, which is basically the plane with maximum margins, among the two groups of the training samples inside the feature space by spotlighting the training cases located at the edge of the class descriptors. This methodology advantageous because an optimal hyperplane is achieved and less training samples are effectual. Thereby it is possible to develop classification with excellent precision with small training sets. SVM engages huge set of non-linear features which is independent of task.1,2,3

SVM is applicable in wide array like recognition, reliability evaluation, bioinformatics and drugs, for categorization of survival time and evaluation of the severity of many acute and chronic diseases like heart diseases and cancer.4While it has several advantages the weak spotis the necessity to select a adequate kernel function to get good results.1

References

1. Janardhanan, Padmavathi&Heena, L. &Sabika, Fathima. (2015). Effectiveness of Support Vector Machines in Medical Data mining. Journal of Communications Software and Systems. 11. 25-30. 10.24138/jcomss.v11i1.114.

2. Battineni, G., Chintalapudi, N., &Amenta, F. (2019). Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). Informatics in Medicine Unlocked, 100200.

3. Asl BM, Setarehdan SK, Mohebbi M. Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. ArtifIntell Med. 2008 Sep;44(1):51-64

4. Son YJ, Kim HG, Kim EH, Choi S, Lee SK. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform Res. 2010 Dec;16(4):253-9.