Building interpretable predictive model for hospital readmission.

Hospital readmission poses a significant cost for healthcare systems worldwide. If patients at a higher risk of readmission could be identified at the outset, appropriate plans to reduce the risk of readmission could be implemented. It is crucial to predict the right target patients and provide interpretable insights from the model's predictions. This study develops a hospital readmission prediction framework using random forest, a widely-used machine learning classifier. Association rule mining (ARM) was employed to identify hidden patterns and relationships among readmission factors. Regarding ARM model interpretation, the overall dataset demonstrated that the main rules for readmission were associated with multiple past hospitalisations and hospital visits, particularly among the elderly. This framework focuses on predictive modelling and provides model interpretation insights that can aid in decision-making. [ABSTRACT FROM AUTHOR]