A Study on Explainable Inference Prediction of Diabetes Complications Based on Medical Knowledge Graph

This paper presents a LightGBM-SMOTE-ENN model that uses a medical knowledge graph to predict diabetes complications with improved accuracy and interpretability. In response to the public health challenge posed by diabetes, this research utilizes advanced AI to analyze medical data, integrating patient information with symptom vectors from the knowledge graph to develop a reliable classification tool. The model's effectiveness is demonstrated through superior performance metrics such as accuracy, recall, and F1 score, attributed to a SHAP value-based method for interpretability. Future directions include expanding the knowledge graph and optimizing algorithms for broader application. This work not only advances diabetes complication prediction but also leverages medical knowledge graphs for clinical support, aiming to enhance healthcare quality and patient outcomes.