INVESTIGATION OF DIFFERENT MACHINE LEARNING CLASSIFIERS FOR TIMELY FORECAST OF BREAST CANCER USING WISCONSIN DATASET

Abstract

The main aims, methods, and results of the study on breast cancer detection using various machine learning classifiers. It seems that the study focused on analyzing the performance of different classifiers such as Logistic Regression, KNN, SVM-LC, SBM-RBF, Gaussian Naïve Bayes, Decision Tree, and Random Forest Classifier on the Wisconsin dataset. The study aimed to measure the accuracy of these classifiers in detecting breast cancer at an early stage. The Wisconsin dataset is a well-known dataset frequently used for breast cancer research and contains relevant features for classification. According to the testing accuracy results you provided, each classifier achieved the following accuracy scores: Logistic Regression=0.9440, K Nearest Neighbor=0.9580, Support Vector Machine (Linear Classifier) =0.9650, Support Vector Machine (RBF Classifier) =0.9650, Gaussian Naïve Bayes=0.9230, Decision Tree=0.9510 and Random Forest Classifier=0.9650. Based on these accuracy outcomes, it can be concluded that the proposed machine learning models, particularly Support Vector Machines (both linear and RBF), as well as K Nearest Neighbor and Random Forest Classifier, performed well in classifying breast cancer using the Wisconsin dataset. Logistic Regression, Decision Tree, and Gaussian Naïve Bayes also achieved reasonably good accuracy scores. The study suggests that the proposed models have the potential to assist medical professionals in accurately classifying breast lesions, which can lead to early detection and better management of breast cancer.