Assessing the Resilience of Machine Learning Models in Predicting Long-Term Breast Cancer Recurrence Results

Breast cancer is still one of the leading causes of death and disability among females globally, and China is quickly becoming one of the leading countries in terms of both incidence and mortality rates. Recurrence is still a concern for many patients, especially those with triple-negative or hormone receptor-positive breast cancer, even though screening and therapy options have improved. More precise and tailored methods are required as traditional prognostic models based on clinical factors often do not accurately forecast recurrence. Using clinical and pathological data from 200 patients, this research assesses how well machine learning algorithms predict the outcomes of long-term breast cancer recurrence. Some of the challenges are data heterogeneity, insufficient amounts of related samples with imbalanced classes, ability to interpret complex models, and model’s interchangeability across the range of patient cases. Metrics including recall, specificity, accuracy, and F1 score are used to evaluate four popular machine learning models: neural networks, decision trees, K-Nearest Neighbors (K-NN), and logistic regression. When it comes to classification accuracy $(\mathrm{CA}=0.94)$ and discriminatory ability $(\mathrm{AUC}=0.98)$, the findings show that the neural network model performs the best overall. Afterwards, K-NN and logistic regression come in second and third, respectively, behind the decision tree model. The results of this study highlight the promise of machine learning algorithms in guiding tailored treatment plans for breast cancer patients and improving their prognosis. Improved recurrence prediction and management allows healthcare practitioners to better allocate resources and offer optimal patient care, leading to advancements in breast cancer research and therapy.