Construction of a pregnancy prediction model in acupuncture treatment for diminished ovarian reserve based on machine learning

Objective

To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve (DOR) based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pregnancy outcomes in DOR.

Methods

We enrolled 377 DOR patients treated with acupuncture and with records of pregnancy outcomes (139 cases of pregnancy and 238 cases failed) exported from the International Patient Registry Platform of Acupuncture-moxibustion (IPRPAM). The predictive variables were determined using Spearman's correlation analysis and feature engineering methods. The model was constructed by adopting logistic regression, naïve Bayes, random forest, support vector machine, extreme gradient boosting, the k-nearest neighbor algorithm, linear discriminant analysis, and neural network methods. The models were validated by the area under the curve (AUC), accuracy (ACC), and importance sequencing, and individual pregnancy prediction was conducted for the best-performing model.

Results

The key factors determining pregnancy after acupuncture in patients with DOR were age, luteinizing hormone (LH) level after treatment, follicle-stimulating hormone (FSH) level after treatment, the ratio of FSH to LH (FSH/LH) after treatment, and history of acupuncture treatment. Random forest model ACC was 0.95, Fβ was 0.93, Logloss was 0.30, Logloss value was the lowest, the model variables exhibited the highest accuracy and precision.

Conclusion

The random forest model for the effects of acupuncture on pregnancy outcomes in patients with DOR, constructed based on the IPRPAM, presents a favorable value for clinical application.

Trial registration: Registration number in the Chinese Clinical Trial Registry Center

ChiCTR2200062293.