Classification of surgical and recovery durations in healthcare settings utilizing machine learning models: A case study at Koç University Hospital

Efficient operating room (OR) scheduling is essential for minimizing patient wait times, optimizing resource use, and reducing operational costs. However, accurately predicting surgery and postoperative recovery durations is challenging due to patient variability, procedural complexity, and limited data availability. This study addresses these challenges by converting continuous duration data into categorical intervals, enabling classification-based machine learning methods suitable for small datasets. Using real-world data from Koç University Hospital, several algorithms—including Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machines, K-Nearest Neighbors, Decision Trees, and ensemble combinations—were evaluated under Random Oversampling and Synthetic Minority Oversampling Technique strategies. Results showed that ensemble models consistently outperformed individual classifiers. For surgery time prediction, the best ensemble achieved 0.71 accuracy and 0.698 F1 score, while Random Forest reached 0.79 accuracy and 0.768 F1 score for recovery time. SMOTE proved effective in mitigating class imbalance, improving recall and F1 scores across models. Targeted feature grouping further enhanced interpretability and predictive reliability. This study provides a practical, data-driven framework for hospitals with limited records to improve OR scheduling. Reliable categorical predictions of surgery and recovery durations can enhance resource allocation, reduce scheduling conflicts, and improve patient outcomes. Keywords: Surgical duration prediction, recovery time prediction, machine learning, classification algorithms, SMOTE, ensemble models, healthcare resource optimization