The Breast Cancer Recurrence Prediction

Abstract

Breast Cancer Recurrence Remains One Of The Most Critical Challenges In Long-term Oncology Care, Posing Significant Risks Even After Successful Primary Treatment. While Advances In Early Diagnosis And Therapeutic Interventions Have Improved Survival Rates, The Ability To Accurately Predict Recurrence Continues To Be Limited By Complex Biological Variability And Reliance On Subjective Clinical Judgment. This Paper Presents An Intelligent Breast Cancer Recurrence Prediction System Leveraging Machine Learning Techniques To Classify Patients Into Low-risk And High-risk Recurrence Groups. The Proposed System Integrates Clinical, Pathological, And Molecular Features—including Tumor Size, Lymph Node Involvement, Hormone Receptor Status, HER2 Expression, And Patient Demographics—within A Structured Data-driven Framework. Multiple Supervised Learning Algorithms Are Trained And Evaluated To Identify The Most Reliable Predictive Model. Experimental Results Demonstrate That Ensemble-based Classifiers Achieve Superior Performance In Terms Of Accuracy, Precision, Recall, And ROC-AUC. The System Aims To Support Oncologists In Personalized Treatment Planning, Proactive Monitoring, And Improved Post-therapy Decision-making, Thereby Enhancing Long-term Patient Outcomes In Clinical Practice.

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