An Improved Stacking Method for Inferring Coronary Heart Disease Based on Tongue Image Information

This study investigates the feasibility of using traditional Chinese medicine (TCM) tongue features for diagnosing coronary heart disease (CHD). The aim is to propose an improved prediction method for CHD diagnosis. We collected data from 255 CHD patients and 308 non-CHD patients, including basic information and tongue image data. Using nine classic classifiers such as XGBoost and Random Forest, we evaluated their performance. To enhance prediction accuracy, we introduced an improved stacking model based on feature partitioning. This model partitions the data through a two-layer network, where the first layer extracts distribution features, and the second layer performs the final prediction. The results show that the proposed method achieved an F1 score of 85.20%, an AUC of 86.00%, a recall rate of 88.46%, an accuracy of 85.80%, a precision of 82.10%, an LR + of 5.39, and an LR- of 0.14. These findings suggest that using tongue image data for CHD prediction is effective, and the proposed method could serve as a valuable tool for non-invasive early detection, warranting further research and validation.