Optimizing Breast Cancer Prediction by Applying Machine Learning
Abstract
In 2015, breast cancer ranked among the most prevalent and fatal cancers affecting women globally. Artificial intelligence is urgently needed to help medical professionals make more accurate decisions, reduce overdiagnosis, and streamline the diagnostic process. This study will implement and perform a comparative study of selected machine learning techniques algorithms, with a focus on SVM, XGBoost, and ANN, with various parameter combinations on the breast cancer dataset. Performance metrics such as accuracy, precision, recall, and F1-score were employed to evaluate and compare the algorithms. The results of this study show that the best model for predicting chronic breast cancer disease, which can help medical professionals predict chronic disease so that it can be treated quickly and accurately, is the SVM method using 8 parameters without the mitosis parameter: Clump thickness, Cell Size Uniformity, Cell Shape Uniformity, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, and Normal Nuclei, with an accuracy value of 0.96 and a sensitivity value of 0.98.
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