Robust Breast Cancer Prognosis Prediction: Adaptive Outlier Removal using SVM and K-Means Clustering

Abstract

Analyzing several datasets is essential to breast cancer research in order to find trends and prognostic markers. For this reason, the Wisconsin Prognostic Breast Cancer (WPBC) dataset offers a valuable source of data. Outliers, however, have the potential to seriously affect how accurate predictive models are. This work suggests using the Support Vector Machine (SVM) algorithm in an adaptive outlier removal method to improve the resilience of prediction models that were trained on the WPBC dataset. To ensure optimum SVM performance, the technique includes pre-processing processes, including addressing missing data and standardizing features. Tailored elimination of outliers is made possible by their dynamic identification, depending on how they deviate from the support of the SVM model. To increase generalization, the SVM is then retrained using the outlier-adjusted dataset. Test set evaluation shows the effectiveness of the method with improved F1-score, recall, and accuracy. With datasets similar to WPBC, this adaptive outlier elimination technique offers a useful tool for improving breast cancer prediction models, leading to increased model performance and dependability in prognostic tasks.