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- W4312453734 abstract "This research is to predict chronic kidney cancer using RBF SVM compared with random forest algorithm. Materials and Methods: A total of 280 samples are collected from the Vermont Center for Ecostudies (VCE) repository. These samples are divided into two types. They are training samples (n=750 (75 %)) and test samples (n=250 (25 %)). For the total sample size, the minimum power (G-Power) required is 0.8. Accuracy is calculated by a novel RBF SVM algorithm. Results: The value 0.8 is taken as G power. Novel RBF SVM obtained accuracy, recall and F-score of 99.0 %, 98.3 % and 98.9 % and Random forest achieved 98.0 %, 97.2 % and 98.3 %. The significance value is less than 0.05. Conclusion: From results it is observed that proposed radial basis function support vector machine algorithm is good in performance compared with random forest algorithm." @default.
- W4312453734 created "2023-01-04" @default.
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- W4312453734 date "2022-07-15" @default.
- W4312453734 modified "2023-10-18" @default.
- W4312453734 title "Prediction of Chronic kidney cancer using RBF support vector machine compared with Random forest for better accuracy" @default.
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- W4312453734 doi "https://doi.org/10.1109/icses55317.2022.9914342" @default.
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