An Artificial Intelligence Model for Predicting Hospital Readmission Using Electronic Health Records Data

This study investigates the application of a machine learning model—specifically the Light Gradient Boosting Machine (LightGBM)—to predict 30-day hospital readmissions using structured electronic health record (EHR) data. Hospital readmissions remain a critical challenge in healthcare systems, often indicating gaps in continuity of care and contributing to higher costs. By leveraging demographic, clinical, and diagnostic variables from 350 anonymized patient records, the model aimed to accurately identify individuals at high risk of readmission. Key features included age, number of previous admissions, length of stay, number of medications, chronic disease status, and gender. Data preprocessing, model training, and evaluation were conducted using Python-based libraries, ensuring both reproducibility and scalability. The model achieved a ROC AUC of 0.89, precision of 0.78, recall of 0.65, and F1 score of 0.71, indicating strong performance and balance between sensitivity and specificity. A confusion matrix analysis confirmed high accuracy in both positive and negative predictions. SHapley Additive exPlanations (SHAP) values were used to enhance interpretability by quantifying the co