首页|Researchers from GITAM University Report Recent Findings in Support Vector Machines (Rg-svm: Recursive Gaussian Support Vector Machine Based Feature Selection Algorithm for Liver Disease Classification)
Researchers from GITAM University Report Recent Findings in Support Vector Machines (Rg-svm: Recursive Gaussian Support Vector Machine Based Feature Selection Algorithm for Liver Disease Classification)
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NETL
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Springer Nature
A new study on Support Vector Machines is now available. According to news reporting from Bengaluru, India, by NewsRx journalists, research stated, “Health is an essential concern for everyone, so it is necessary to facilitate medical services that are easily accessible to everyone. The primary goal of this work is to predict liver diseases using a machine-learning strategy that makes use of feature selection and classification techniques.” The news correspondents obtained a quote from the research from GITAM University, “This work proposes the recursive Gaussian support vector machine-based feature selection (RG-SVM) algorithm. It uses the Gaussian kernel of support vector machine and recursive feature selection algorithm for the prediction of liver disease. The proposed RG-SVM algorithm has been evaluated on the Indian liver patient records dataset. Various classification algorithms such as logistic regression, decision tree, k-nearest neighbour, and Naive Bayes are implemented and compared in order to assess the accuracy, confusion matrix and area under curve. The proposed RG-SVM has been compared with other existing algorithms such as logistic regression (LR), decision tree (DT), k-nearest neighbour (KNN), Naive Bayes (NB), and proposed RG-SVM algorithms. The algorithms LR, DT, KNN, NB, and proposed RG-SVM have accuracy values of 73, 80, 81, 54, and 93%, respectively. It clearly shows that the proposed RG-SVM with the support of a recursive feature selection algorithm, outperformed other existing algorithms with an improved accuracy of 14 - 39% 12-20% of reduced MSE error over other compared algorithms. Similarly, the sensitivity and specificity of RG-SVM algorithm produced 5-26% and 34-72% improved results over the existing algorithms.”
BengaluruIndiaAsiaAlgorithmsEmerging TechnologiesMachine LearningSelection AlgorithmSupport Vector MachinesVector MachinesGITAM University
NETL
NSTL
Springer Nature
