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

Background/Aim: 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. Patients and Methods: 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. Results: 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). Conclusion: 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.