A hybrid framework based on knowledge distillation for explainable disease diagnosis

Deep learning (DL) techniques provide highly accurate disease predictions by extracting implicit higher-order correlations between features. However, DL opinions are not advisable for direct clinical practice because they lack interpretability in their reasoning logic. Fuzzy rule-based systems (FRBSs) can provide transparent and effective diagnostic results, but the rule explosion problem and the long-tail distribution of rules weaken their performance. Therefore, DL and FRBS complement each other. This paper proposes a hybrid framework based on DL models and fuzzy rules for explainable disease diagnosis. This framework has three components: interpretable component, uninterpretable component, and knowledge distillation component. In the interpretable component, we propose a strong rule extraction method to avoid the rule explosion problem and the long-tail distribution of rules by discarding weak rules. In the uninterpretable component, we use DL models to capture the implicit higher-order interactions between features to improve the model's performance. The knowledge distillation component embeds the hidden knowledge extracted from DL models into SFRBSs to guide the parameter tuning. This framework can predict new instances and provide interpretable prediction results by extracting explicit knowledge (fuzzy rules) and feature weights. Experimental results based on three real-world medical datasets show that the framework achieves the highest AUC and accuracy, which is more accurate and interpretable than other diagnostic models.