A hierarchical and interpretable machine learning model for acupoint determination

This study used machine learning methods to develop a model that can offer personalized acupoint prescriptions for patients based on their symptoms, enhancing both the efficiency and effectiveness of acupuncture and moxibustion therapy (AMT).

We first preprocessed textual AMT data to build an acupoint prescription database designed for machine learning applications. Then, based on data analysis, we selected the hierarchical classification model hierarchical attention-based recurrent neural network (HARNN) to determine acupoint prescriptions based on symptoms. Computational experiments were conducted using 5-fold cross-validation to evaluate the model's performance, with intersection over union (IoU) as the primary evaluation metric. Finally, to enhance model interpretability, the local interpretable model-agnostic explanation (LIME) method was applied to visualize prediction results and improve its clinical applicability.

On the original dataset of 5000 samples, HARNN achieved an IoU of 0.883 in predicting acupoint prescriptions. After data augmentation, the IoU reached 0.954 in 5-fold cross-validation, and 0.932 on a test set of 1000 original samples. The use of LIME enabled intuitive visualization of the model's prediction rationale, thereby enhancing the model's reliability.

This study developed a hierarchical and interpretable machine learning model that predicts acupoint prescriptions based on symptoms, integrating HARNN for hierarchical classification and LIME for interpretability, which provides an effective technical approach and methodology for the intellectualization of AMT. Please cite this article as: Yang H, Wu R, Nakata M, Ge QW. A hierarchical and interpretable machine learning model for acupoint determination. J Integr Med. 2026; 24(3):340-351.