Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques

Accurate timing prediction of surgery is essential for efficient operating room scheduling and ensuring patient care. This study proposes a two-layered Accurate Surgical Time Prediction (ASTP) framework. The first layer combines feature importance and advanced machine learning models to estimate surgical time. After preprocessing, two complementary interpretable AI methods were used: Long Short Term Memory with SHapley Additive exPlanations (SHAP) values and Random Forest permutation importance, to determine the importance of features. In the second layer, subsets of features (TOP-K) were progressively evaluated using HGBR and compared with multiple models: artificial neural networks (ANNs) and recurrent models (Long Short Term Memory, Gated Recurrent Unit, and hybrid Long Short-Term Memory + Gated Recurrent Unit). The proposed framework was evaluated on two datasets: a real-world dataset from an operating room at Nile Hospital and a public dataset from the Medical Informatics Operating Room Vitals and Events Repository (MOVER). In the Nile Hospital dataset, the results show that the Histogram Gradient Boosting Regression (HGBR) approach achieves the best balance, with a Mean Absolute Error of 8.89 min, Root Mean Square Error of 19.5 min, and R-squared of 0.26 using only four features, outperforming other methods. On the MOVER dataset, HGBR also demonstrated the strongest overall predictive behavior, achieving its best numerical result at TOP-12 and best subset at TOP-7, which preserved near-optimal performance with reduced input complexity. The proposed ASTP framework provides an interpretable and resource-efficient solution for surgical time prediction, supporting more intelligent operating room scheduling and facilitating future integration into hospital decision-making.