Prediction of breast cancer metastasis based on Tri-training using Dempster-Shafer theory

Machine learning applied to the clinical prediction of breast cancer mainly deals with a single view feature, so class imbalance, low classification accuracy and poor generalization performance may occur.

Addressing the aforementioned problems, this study proposes a semi-supervised learning framework for predicting breast cancer metastasis. An integrated over-sampling and under-sampling approach is employed to construct multiple diverse weak classifiers, aiming to mitigate prediction bias towards the majority class and enhance generalization performance. By improving the classical Dempster-Shafer theory, the credibility of evidence sources is dynamically adjusted based on the conflict between them, thereby enhancing the accuracy of multi-source feature fusion. This improved DS theory is integrated with the Tri-training collaborative training strategy. Credibility thresholds are used to generate pseudo-labels from unlabeled data, which expands the labeled dataset, reduces prediction conflicts among multiple models, and improves both prediction accuracy and generalization. Furthermore, the SHAP interpretability method is adopted for feature importance analysis, aiming to identify potential risk factors and provide technical support for the clinical diagnosis of breast cancer.

The 304 clinical cases of breast cancer are from Shanghai Pudong New Area Gongli Hospital. The Accuracy, Precision, Recall, F1-score, and AUC on threefold cross-validation are 94.41, 94.44, 93.51, 93.88 and 93.5% respectively.

The proposed model outperforms conventional supervised machine learning baselines, providing effective technical support for clinical breast cancer diagnosis.