Implementation of SVM machine learning Algorithm to predict lung And Breast Cancer
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Abstract
The proposed work will be implemented and developed. Related to learning algorithms the support vector machine is under supervised learning algorithm. To predict cancers like Breast and lung cancers so many statistical and Machine learning models are there, but out of all available models the super vector machine algorithm is best. Maximum edge of SVM hyperplane and edge prepared in 2 class test. To build an SVM classifier, you first need to define a kernel function. The predicted performance of A may vary. However, there are various studies investigating the characteristics of SVM predictions based on various factors. The proposed model is fully analyzing the predictive performance of SVMs and SVM sets and comparing training with large and small lung cancer datasets with 99.52% accuracy, roc 0.876, and major f 0.996%. SVMS and SVM set time..
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