| Title |
On Application of Machine Learning for Deciding Acupoints in Acupuncture and Moxibustion Treatment |
| Authors |
(Hang Yang) ; (Ren Wu) ; (Mitsuru Nakata) ; (Qi-Wei Ge) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.2.152 |
| Keywords |
Artificial intelligence; Machine learning; Acupuncture and moxibustion; Traditional Chinese medicine |
| Abstract |
This paper discusses a machine learning-based approach to optimize acupuncture and moxibustion treatment (AMT). The goal is to develop a model that can offer personalized acupoints prescriptions for patients based on their symptoms, enhancing both the efficiency and effectiveness of treatment. A database comprising symptoms and acupoints prescriptions for 3,000 disease cases was used, and 11 machine learning algorithms were applied to learn from this data. The training process utilized 90% of the data for 5-fold cross-validation and 10% for testing to assess generalization ability. Intersection over Union (IoU) was chosen as the key evaluation metric for the models. The Seq2seq model with attention mechanism emerged as the best-performing algorithm, achieving an IoU of 95.72% on cross-validation and 95.33% on the test set. These results suggest that using Seq2seq with attention can significantly reduce subjectivity in acupoint selection and increase the efficiency of AMT. This approach provides a promising data-driven method for improving treatment precision and saving time in clinical settings. |