An interpretable imbalance ensemble classification method for readmission risk assessment incorporating multi-view perturbation and SHAP analysis

In the domain of medical services, patients are frequently readmitted shortly after discharge due to inadequate discharge planning or relapses of their illnesses. Such occurrences not only deplete valuable medical resources but also compromise patient satisfaction with the medical care they receive. To address this issue, we propose an interpretable imbalance ensemble classification method incorporating multi-view perturbation to evaluate the risk of patient readmission. Our study introduces a novel multi-view perturbation technique to bolster the model's generalization capabilities. Furthermore, we propose a more robust ensemble strategy based on Evidential Reasoning (EVR) rules, which enhances the stability of the ensemble learning model's fusion outcomes. Additionally, recognizing the impact of sensitive parameters on model performance, we present a parameter optimization approach utilizing the Differential Evolution (DE) algorithm, which balances model predictive accuracy and computational efficiency within the fitness function. Empirical results using real-world medical data indicate that our proposed method accurately identifies patients at high risk of readmission and surpasses current state-of-the-art methods in risk assessment.