Breast Cancer Prediction Optimization Using Support Vector Machine and Naive Bayes Algorithms

Abstract

Breast cancer is ranked as the second most common cause of death for women worldwide. Breast cancer is often found when it has entered the final stage. In general, this is due to slow handling and treatment, so it is very necessary to detect the disease early. The purpose of this study is to determine the performance of the two algorithms, namely Naïve Bayes and Support Vector Machine (SVM) in classifying breast cancer types which will then be analysed and compared the accuracy of the two algorithms. The dataset used in this study, Breast Cancer Wisconsin, is public data originating from UCI Machine Learning, has a total of 683 data with 10 attributes and has two classes, namely benign class with 458 data and malignant class with 241 data. The dataset was split 80:20, with 80% used as training data and 20% as testing data, and then evaluated using cross-validation. The results of the study show that Support Vector Machine (SVM) has the best performance with an accuracy of 96.89% while Naïve Bayes 96.15%. With this accuracy, These results indicate that the SVM model provides better classification performance than Naïve Bayes for the Breast Cancer Wisconsin dataset.