MACHINE LEARNING-BASED PREDICTION OF BREAST CANCER RECURRENCE

Abstract

Breast cancer recurrence remains a major cause of mortality among women worldwide. Early identification is essential for improving patient outcomes, and prediction modelling has emerged as an approach to support this objective. This study aimed to develop and compare machine learning models for predicting breast cancer recurrence. A retrospective secondary analysis was conducted on data from 286 patients in the University of California, Irvine (UCI) Machine Learning Repository. Nine predictors were used to develop logistic regression (LR), artificial neural network (ANN), and extreme gradient boosting (XGBoost) models. The dataset was split into training and testing sets using a 70:30 ratio. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), Brier score, and calibration analysis, with 95% confidence intervals estimated through bootstrap resampling. LR demonstrated the best discriminatory performance, achieving a test AUC of 0.790. In contrast, XGBoost and ANN showed lower generalization performance, with test AUCs of 0.727 and 0.748, respectively. LR also achieved the highest testing recall (0.731) and F1-score (0.623), indicating superior sensitivity for identifying recurrent cases. Meanwhile, XGBoost demonstrated the highest precision (0.727), accuracy (0.756), and calibration performance. The relatively small sample size and reliance on structured clinical predictors may have contributed to the superior performance of LR in this study. In conclusion, LR demonstrated the most reliable predictive performance for this dataset.  Future research should use larger, diverse datasets and incorporate a broader range of predictors, including imaging and genomic data, to optimize the benefits of more complex machine learning models.