Comparative Analysis of Breast Cancer Detection Methods UsingSVM Linear, SVM RBF, SVM Polynomial, WKNN, DWKNN,and ANN

The diagnosis of cancer is a subject of extensive research within the medical field. Numerousscholars have dedicated their efforts to enhancing performance and attaining optimal outcomes.Among the various types of cancer prevalent globally, breast cancer stands out as a leadingcause of mortality. The accurate diagnosis of this particular type of cancer poses a significantchallenge in ongoing research endeavors related to cancer diagnosis. Within the realm ofartificial intelligence, machine learning emerges as a discipline that facilitates the evolution ofmachines through a systematic process. This sophisticated technology finds widespreadapplication in the field of bioinformatics, notably in the context of diagnosing breast cancer.The Support Vector Machine (SVM), Artificial neural networks (ANN) and K NearestNeighbor (KNN) classifier constitute a sophisticated classification method renowned for itsexceptional efficacy across a wide array of applications. The primary aim of this researchendeavor lies in scrutinizing the efficacy of the SVM, ANN and KNN classifier in diagnosingbreast cancer through the analysis of tumor datasets. SVM linear, SVM RBF, SVM Polynomial,DWKNN, WKNN and ANNs methods were applied to the dataset obtained after dimensionreduction, the results were compared and the most successful model was suggested as thedecision support system to be used.

Keywords:

Breast cancer , Support Vector Machine , Artificial neural networks , KNearest Neighbor