Predictive Modeling of Breast Cancer Outcomes Using Supervised Machine Learning Algorithms

Abstract

Breast cancer remains one of the leading causes of mortality among women, emphasizing the need for accurate predictive models to aid in early diagnosis and treatment. This study explores the application of supervised machine learning algorithms to predict breast cancer outcomes, leveraging patient data such as demographics, clinical features, and histopathological information. We evaluate several algorithms, including Logistic Regression, Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines (GBM), to identify their efficacy in predicting survival rates and disease progression. Our results indicate that ensemble methods, particularly Random Forests and GBMs, offer superior predictive performance compared to traditional approaches. This work demonstrates the potential of machine learning techniques to enhance decision-making in breast cancer management, providing a framework for future research and clinical application.