基于机器学习算法构建晚期直肠癌病人 疼痛危象预测模型.
Objective: To construct a prediction model for pain crisis in patients with advanced rectal cancer based on machine learning algorithms and analyze the predictive performance of different models. Methods: A convenience sampling method was used to select 210 patients with advanced rectal cancer admit...
| Publicado en: | Chinese Nursing Research Vol. 39; no. 17; pp. 2900 - 2908 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Chinese Nursing Research Editorial Office
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | Objective: To construct a prediction model for pain crisis in patients with advanced rectal cancer based on machine learning algorithms and analyze the predictive performance of different models. Methods: A convenience sampling method was used to select 210 patients with advanced rectal cancer admitted to our hospital from September 2022 to September 2024 as the study subjects. Questionnaires were administered using the General Information Questionnaire, Social Support-Rating Scale, Connor-Davidson Resilience Scale, and Hospital Anxiety and Depression Scale. Based on whether patients experienced pain crisis, they were divided into a pain crisis group and a non-pain crisis group. Univariate and multivariate analyses were conducted to identify influencing factors of pain crisis. Prediction models were constructed using Logistic regression, random forest, and decision tree algorithms based on the univariate and multivariate analysis results. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate model efficacy and predictive value. Results: Among the 210 patients with advanced rectal cancer, 64 (30.48%) experienced pain crisis. Multivariate analysis showed that social support, psychological resilience, negative emotions, age, monthly household income per capita, and the number of radiotherapy/chemotherapy sessions were independent influencing factors for pain crisis in patients with advanced rectal cancer (all P< 0.05).ROC curve analysis revealed that the AUC values for the Logistic regression model, decision tree model, and random forest prediction model were 0.902, 0.901, and 0.933, respectively. The accuracy rates were 0.881,0.852, and 0.889; sensitivity rates were 0.750,0.734, and 0.824; specificity rates were 0.938,0.904, and 0.913; recall rates were 0.750,0.734, and 0.824; precision rates were 0.842,0.770, and 0.933; and F1 scores were 0.793,0.752, and 0.875, respectively. Except for specificity, the random forest model achieved the highest values in AUC, accuracy, sensitivity, recall, precision, and F1 score, demonstrating the best overall performance. Conclusions: The random forest model demonstrates superior predictive performance for pain crises in advanced rectal cancer patients compared to Logistic regression and decision tree models. Clinically, this model can help identify high-risk patients for early intervention with preventive measures, thereby reducing the incidence of pain crises. 目的: 基于机器学习算法构建晚期直肠癌病人疼痛危象的预测模型并分析不同模型的预测性能。方法: 采用便利抽样法, 选 取2022 年9 月--2024 年9 月我院收治的晚期直肠癌病人210 例为研究对象, 采用一般资料调查表、社会支持量表、心理弹性量表、医 院焦虑抑郁量表进行问卷调查。根据病人是否发生疼痛危象分为发生组和未发生组, 采用单因素及多因素分析疼痛危象的影响因 素。基于单因素及多因素分析结果构建Logistic 回归模型、随机森林模型及决策树模型, 采用受试者工作特征（ROC）曲线下面积 （AUC）分析模型效能及预测价值。结果:210 例晚期直肠癌病人中, 发生疼痛危象病人64 例（30. 48%）。多因素分析结果显示, 社会 支持、心理弹性、负性情绪、年龄、家庭人均月收入、放化疗次数为晚期直肠癌病人发生疼痛危象的独立影响因素（均P<0. 05）。 ROC 曲线分析结果显示, Logistic 回归模型、决策树模型和随机森林模型的AUC 分别为0. 902, 0. 901, 0. 933, 准确度分别为0. 881, 0. 852, 0. 889, 灵敏度分别为0. 750, 0. 734, 0. 824, 特异度分别为0. 938, 0. 904, 0. 913, 召回率分别为0. 750, 0. 734, 0. 824, 精确率分 别为0. 842, 0. 770, 0. 933, F1 值分别为0. 793, 0. 752, 0. 875。随机森林模型除特异度外, AUC、准确度、灵敏度、召回率、精确率及F1 值均为最高, 综合表现最优。结论: 随机森林模型对晚期直肠癌病人疼痛危象的预测性能优于Logistic 回归模型及决策树模型, 临床 可据此识别疼痛危象高风险病人, 早期予以相关预防措施, 降低疼痛危象发生率. |
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