Implementation of SVM machine learning Algorithm to predict lung And Breast Cancer
Main Article Content
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..
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
