in the opening vignette SVM method was the best in both accuracy of predicted outcomes and sensitivity.
<h3>What is SVM method? </h3>
Support vector machines (SVMs, also known as support vector networks) in machine learning are supervised learning models with corresponding learning algorithms that evaluate data for regression and classification. Developed by Vladimir Vapnik and colleagues at AT&T Bell Laboratories (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995,Vapnik et al., 1997[citation needed]) SVMs, which are based on statistical learning frameworks or the VC theory put out by Chervonenkis and Vapnik (1982, 1995), are among the most reliable prediction techniques (1974). An SVM training technique creates a model from a set of training examples, each of which is designated as belonging to one of two categories, making it a non-probabilistic binary linear classifier (however strategies such Platt scaling may be used).
SVM assigns training samples to spatial coordinates in order to maximise the distance between the two categories. Then, based on which side of the gap they fall, new samples are projected into that same area and predicted to belong to a category.
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