Construction of a prognostic assessment model of acupuncture intervention for lumbar disc herniation based on interpretable machine learning algorithms

WANG Zhe; NING Yike; CUI Huafeng; LIU Xingyu; DING Ruixin; JIANG Guoliang; SHENG Chunyue; HAN Jing

doi:10.13703/j.0255-2930.20250510-k0001

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Construction of a prognostic assessment model of acupuncture intervention for lumbar disc herniation based on interpretable machine learning algorithms

Acupuncture and Intelligent Medicine | 更新时间：2026-05-12

- Construction of a prognostic assessment model of acupuncture intervention for lumbar disc herniation based on interpretable machine learning algorithms
- Chinese Acupuncture & Moxibustion Vol. 46, Issue 5, Pages: 678-686(2026)
- 作者机构：
  
  山东中医药大学，针灸推拿学院，济南　250355
  山东中医药大学，护理学院，济南　250355
  山东中医药大学附属医院针灸科，济南　250013
  德州市妇幼保健院中医科
- 作者简介：
- 基金信息：
- DOI：10.13703/j.0255-2930.20250510-k0001
  CLC：
- Received：10 May 2025，
  
  Online First：26 March 2026，
  
  Published：12 May 2026
- 稿件说明：
移动端阅览
WANG Zhe, NING Yike, CUI Huafeng, et al. Construction of a prognostic assessment model of acupuncture intervention for lumbar disc herniation based on interpretable machine learning algorithms[J]. Chinese Acupuncture & Moxibustion, 2026, 46(5): 678-686.
DOI：

WANG Zhe, NING Yike, CUI Huafeng, et al. Construction of a prognostic assessment model of acupuncture intervention for lumbar disc herniation based on interpretable machine learning algorithms[J]. Chinese Acupuncture & Moxibustion, 2026, 46(5): 678-686. DOI： 10.13703/j.0255-2930.20250510-k0001.

摘要

目的：

构建并验证针刺干预腰椎间盘突出症（LDH）的预后评估模型，分析影响针刺疗效的关键因素，为临床个体化治疗方案提供决策支持。

方法：

回顾性分析478例LDH患者临床资料，采用最小绝对收缩与选择算子（LASSO）回归筛选预测变量，构建决策树、随机森林、极端梯度提升、支持向量机、多层感知机、Logistic回归、轻量级梯度提升机及K近邻8种机器学习预测模型。通过五折交叉验证评估各模型性能，采用SHapley加性特征解释方法（SHAP）分析模型可解释性，并开发基于Shiny的在线交互式应用。

结果：

LASSO回归筛选出15个预测变量；支持向量机模型在五折交叉验证中表现最优，平均受试者工作特征曲线下面积（ROC AUC）为0.862，平均Brier评分为0.157；决策曲线分析显示该模型具有良好临床应用价值。SHAP分析显示：针刺联合浮针、温针灸、电针及每日针刺1次与预后良好相关；体质量指数（BMI）值越高、从事重体力工作、应用糖皮质激素和高渗脱水药及不同节段椎间盘突出、椎管狭窄与预后不良相关。

结论：

基于可解释性机器学习算法的针刺干预LDH预后评估模型预测性能良好，可为临床个体化治疗方案选择提供依据，但后续仍需更多外部验证加以优化。

Abstract

Objective

To construct and validate a prognostic assessment model of acupuncture intervention for lumbar disc herniation (LDH)

and analyze the key factors of acupuncture efficacy

so as to provide the decision support for clinical individualized treatment.

Methods

Clinical data of 478 LDH patients were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictive variables

and 8 machine learning prediction models were constructed

including decision tree

random forest

extreme gradient boosting

support vector machine

multilayer perceptron

logistic regression

light gradient boosting machine

and K-nearest neighbor. The performance of each model was evaluated through five-fold cross-validation. SHapley additive exPlanations (SHAP) method was employed to analyze model interpretability

and an online interactive application based on Shiny was developed.

Results

LASSO regression selected 15 predictive variables; the support vector machine performed the best in five-fold cross-validation

with an average area under the receiver operating characteristic curve (AUC) of 0.862 and an average Brier score of 0.157. Decision curve analysis indicated a good clinical application value for this model. SHAP analysis showed that the combined therapies of acupuncture with

ʹs acupuncture

warm needling

electroacupuncture and acupuncture delivered once daily were associated with favorable prognosis; and the higher body mass indexes (BMI)

engagement in heavy physical labor

and the use of glucocorticoids and hyperosmotic dehydration agents

as well as the disc herniation of different segments and spinal canal stenosis were associated with unfavorable prognosis.

Conclusion

The interpretable machine learning-based prognostic assessment model of acupuncture intervention for LDH demonstrates a good predictive performance

providing evidence for clinical individualized treatment. However

more external validations are required for its optimization.

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