A Multi-Head Attention Soft Random Forest for Interpretable Patient No-Show Prediction

Abstract

Unattended scheduled appointments (“patient no-shows” henceforth) adversely affect healthcare providers and patients’ health, disrupting the continuity of care, operational efficiency, and allocation of medical resources. Therefore, accurate predictive modeling is needed to reduce the impact of patient no-shows. Although machine learning methods, such as logistic regression, random forests, and decision trees, are widely used to predict patient no-shows, they often rely on hard decision splits and static feature importance, limiting adaptability to complex patient behaviors. To address this limitation, we propose a hybrid multi-head attention soft random forest (MHASRF) model that integrates attention mechanisms into a random forest using probabilistic soft splitting. It assigns attention weights across the trees, enabling attention on specific patient behaviors. The MHASRF model exhibited an accuracy of 88.24%, specificity of 91.21%, precision of 81.60%, recall of 82.01%, F1-score of 81.81%, and area under the receiver operating characteristic curve of 94.07%, demonstrating high and balanced performance across metrics. It could also identify key predictors of patient no-shows at two feature-importance levels (tree and attention mechanism), providing deeper insights into patient no-shows. Thus, the proposed MHASRF model is a robust, adaptable, and interpretable method for predicting patient no-shows that can help healthcare providers optimize resources.