Predicting 14-day unplanned hospital readmissions using machine learning and explainable artificial intelligence

Unplanned readmission within 14 days is a critical indicator of healthcare quality, impacting patient risk, costs, and hospital reputations. This study explores the use of machine learning to predict unplanned hospital readmissions within 14 days and explainable artificial intelligence techniques to identify key risk factors. Patient data, such as age, gender, and hospital stay length, were used to create a prediction model based on artificial neural networks. Techniques like class weighting were applied to improve the prediction of less common cases. Shapley Additive Explanations and Integrated Gradients methods were used to explain the model, making it easier to understand and use in clinical settings. The results show that the model improves the accuracy of readmission risk predictions, helps healthcare professionals find high-risk patients early, and supports timely interventions to improve care quality and reduce readmissions.