Predicting patient no-shows in magnetic resonance imaging appointments using interpretable machine learning

Abstract

Missed appointments, or no-shows, in Magnetic Resonance Imaging (MRI) procedures lead to underutilised resources and scheduling inefficiencies in healthcare. This study proposes an interpretable machine learning framework to predict patient no-shows using real-world data from a brazilian diagnostic imaging clinic (n = 28991; no-show rate = 8.2%). We evaluated Logistic Regression, Multilayer Perceptron, XGBoost, CatBoost, and LightGBM, incorporating both patient-level and clinic-level features, including historical demand and no-show trends. Time-split cross-validation was used for hyperparameter tuning, selecting models based on AUC-PR, and for adjusting the probability threshold using the 𝐹𝛽-score with 𝛽 = 1.5. The LightGBM model was selected and achieved an AUC-PR of 0.2203 and AUC-ROC of 0.6559 on test data, with Precision = 19.68%, Sensitivity = 39.63%, 𝐹1-score = 26.30% and overall accuracy = 82.37%. SHAP values were used to interpret feature contributions, revealing that variables such as patient age, sex, distance to clinic, healthcare plan, waiting time, and no-show history were most influential. This framework enables data-driven overbooking strategies and personalised reminders, aligning with operational goals and minimising resource waste. Our approach demonstrates that interpretable models can support clinical decision-making in realistic environments, with potential extensions to other healthcare domains.