Machine Learning-based Prediction of Unplanned Acute Care in Outpatients Receiving S-1 Chemotherapy

The increasing use of oral anticancer agents in outpatient settings has led to a growing need for unplanned acute care (UAC) due to treatment-related adverse events. Early identification of high-risk patients is therefore clinically important. This study aimed to develop a machine learning-based model to predict UAC in outpatients receiving S-1 chemotherapy and to compare its performance with conventional logistic regression.

This retrospective single-center study included 579 outpatients who newly underwent S-1 therapy. UAC was defined as chemotherapy-related unplanned hospitalization or urgent outpatient visits during the first treatment course. Predictive models were developed using logistic regression and machine-learning algorithms, including a support vector machine (SVM). Model performance was evaluated using recall-oriented metrics, with the F2 score adopted as the primary performance measure. Shapley additive explanations (SHAP) were applied for feature selection and model interpretation.

Among the 579 patients, 45 experienced UAC. In the independent test dataset (n=173), the SHAP-selected SVM model demonstrated superior performance compared with logistic regression, achieving higher recall (0.769 vs. 0.615) and F2 score (0.368 vs. 0.325).

An SVM-based machine-learning model improved the prediction of UAC among outpatients receiving S-1 chemotherapy by reducing false-negative predictions and may support early risk stratification to enhance the safety of outpatient chemotherapy.