Predicting Hospital Stay Length Using Explainable ML
DOI:
https://doi.org/10.46647/rdems0205041Keywords:
Hospital Length of Stay (LOS), Explainable Machine Learning (XML), Healthcare Analytics, Predictive Modeling, Clinical Decision Support Systems, Electronic Health Records (EHR), Feature Importance Analysis, Patient Risk Stratification, Medical Data Mining, Supervised Learning Algorithms, Model Interpretability, SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), Healthcare Resource Optimization, Patient Outcome Prediction.Abstract
Predicting the length of hospital stay (LOS) is a critical task in healthcare management, as it directly impacts hospital resource allocation, patient care, and operational efficiency. This project presents a machine learning-based approach to accurately predict the duration of a patient’s hospital stay using clinical and demographic data. Traditional methods rely heavily on physician estimates, which may be subjective and inconsistent. The proposed system utilizes historical patient data, including medical history, diagnosis, lab results, and treatment plans, to train predictive models. Various machine learning algorithms such as Linear Regression, Decision Trees, and Random Forest are employed to analyze patterns and forecast LOS. The system processes data through preprocessing, feature selection, and model training phases to ensure high prediction accuracy. By providing early predictions, hospitals can optimize bed management, reduce overcrowding, and improve patient flow. The system also supports healthcare professionals in decision-making by offering data-driven insights. Ultimately, this solution enhances operational efficiency, reduces healthcare costs, and improves patient satisfaction. The integration of predictive analytics into healthcare systems represents a significant step toward intelligent and efficient hospital management.