Predicting Hospital Stay Length Using KNN Regressor Optimized with Gridsearchcv: An Explainable Machine Learning Approach

Efficient bed management is essential for minimizing hospital costs, improving efficiency, and enhancing patient outcomes. This study introduces a predictive framework for forecasting hospital-ICU length of stay (LOS) at admission, leveraging hospital EHR data. Unlike prior work, which heavily relied on advanced tree-based models, this research proposes a K-Nearest Neighbors (KNN) model with hyperparameter optimization using GridSearchCV for predicting ICU patients’ LOS. The KNN model effectively classifies patients into short and long LOS categories by learning patterns in clinical information systems (CIS). To ensure robustness, we evaluated the model using various performance metrics, including Accuracy, AUC, Sensitivity, Specificity, F1-score, Precision, and Recall. The optimized KNN model demonstrated competitive predictive performance with improved interpretability compared to traditional complex models. Additionally, explainable artificial intelligence (xAI) techniques were incorporated to provide transparent insights into the decision-making process, further enhancing the trustworthiness of the predictions. This work highlights the potential of using machine learning models like KNN for reliable, interpretable, and efficient ICU LOS prediction, aiding hospitals in improving resource allocation and patient care outcomes.