Breast Cancer Risk Prediction Using Machine Learning Algorithms

Breast cancer poses a major health risk for women due to its high incidence and fatality rate.The absence of highly reliable prognosis systems makes it difficult for clinicians to formulate treatment strategies that can effectively improve survival outcomes.Therefore, there is an urgent need to develop predictive approaches that ensure high accuracy with minimal error rates.This study compares four widely used machine learning algorithms-Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (KNN), and Random Forest (RF)-for breast cancer outcome prediction across multiple datasets.All experimental evaluations were performed in a simulated environment using the Jupyter Notebook computing platform.The research objectives are grouped into three key areas: (1) risk estimation prior to clinical diagnosis, (2) prediction of diagnostic results and treatment guidance, and (3) outcome assessment throughout the treatment process.The proposed framework supports performance comparison of different predictive techniques, enabling method selection based on clinical needs.This work primarily evaluates model accuracy, while future studies may extend to additional clinical parameters, allowing breast cancer research to be classified based on broader performance and medical indicators.