Objective

2

To construct and validate a predictive model for the therapeutic effect o

f acupuncture at

Tongdu Yangxin

prescription (acupoint prescription for promoting the circulation of the governor vessel and nourishing the heart) on insomnia

so as to develop an open-access interactive artificial intelligence (AI)-assisted decision-making platform.

Methods

2

Clinical data of 139 insomnia patients treated with

Tongdu Yangxin

acupuncture therapy were included. All the patients had received acupuncture at Baihui (GV20)

Yintang (GV24

+

)

bilateral Shenmen (HT7)

and bilateral Sanyinjiao (SP6); and electric stimulation was attached to Baihui (GV20) and Yintang (GV24

+

)

using a continuous wave and a frequency of 2 Hz. The treatment was delivered once every other day

3 treatments a week

and for 2 consecutive weeks. Patients with Pittsburgh sleep quality index (PSQI) score reduction rate

<

50% were classified as the "no response group"

and those with ≥50% were as the "response group". Outliers were addressed using the 1.5×IQR rule

and missing values were imputed via predictive mean matching. Key features were selected by intersecting the feature importance results from eXtreme Gradient Boosting (XGBoost) and random forest algorithms. After balancing class distribution using the Synthetic Minority Over-sampling Technique (SMOTE)

20% of the data was reserved as a validation set. The remained data underwent the stratified sampling iterations to generate 200 pairs of 3∶1 training-test sets

which was employed for training and internal validation of 8 machine learning algorithms. The optimal algorithm and data partitioning strategy were selected to construct the final model

followed by external validation. The best-performing model was deployed online via Streamlit to create an interactive AI platform.

Results

2

Key predictive features for model construction included insomnia duration

the total PSQI score

PSQI sleep efficiency subscore

the proportion of N1 and N2 sleep stages in total

sleep duration

and the maximum pulse rate during sleep. The CatBoost-based model achieved an AUC of 0.92

the average precision of 0.77

and accuracy

average recall

and average F1-score of 0.75 on the test set. On the validation set

it attained an AUC of 0.84

with accuracy

average precision

average recall

and average F1-score all at 0.72

demonstrating robust predictive performance. An interactive AI platform was subsequently developed (

https://tdyx-catboost.streamlit.app/

https://tdyx-catboost.streamlit.app/

).

Conclusion

2

This study successfully establishes and validates a CatBoost-based efficacy prediction model for

Tongdu Yangxin

acupuncture therapy in treatment of insomnia. The developed AI platform provides data-driven decision support for acupuncture-based insomnia management.