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Construction of a prediction model for the preoperative frailty risk of breast cancer patients based on interpretable machine learning algorithms
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Qing ZHANG1, Ying ZHANG2, Jianli DONG3, Wenlong YU2, Yinhuan XIONG2, Jiayue ZHAO1, Hongmei XU1
Chinese Nursing Research | 2026, 40(1) : 60 - 70
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Chinese Nursing Research | 2026, 40(1): 60-70
• Original Articles •
Construction of a prediction model for the preoperative frailty risk of breast cancer patients based on interpretable machine learning algorithms
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Qing ZHANG1, Ying ZHANG2, Jianli DONG3, Wenlong YU2, Yinhuan XIONG2, Jiayue ZHAO1, Hongmei XU1
Affiliations
  • 1School of Nursing, Binzhou Medical University(School of Gerontology), Shandong 256600 China
  • 2Binzhou People's Hospital
  • 3Affiliated Hospital of Binzhou Medical University
Published: 2026-01-10 doi: 10.12102/j.issn.1009-6493.2026.01.008
Outline
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Objective

To analyze the influencing factors of preoperative frailty in breast cancer patients and to develop a risk prediction model.

Methods

A total of 583 inpatients scheduled for surgical treatment in the breast surgery departments of two Grade A tertiary hospitals in Binzhou city,Shandong province,were selected between August 2024 and January 2025.They were randomly divided into a training set(408 cases) and a validation set(175 cases) in a 7∶3 ratio.Variables were screened using both Lasso regression and the Boruta algorithm.Four machine learning algorithms-support vector machine,decision tree,light gradient boosting machine,and extreme gradient boosting-were employed to develop prediction models.Model performance was compared based on accuracy,precision,sensitivity,specificity,F1-score,and the area under the receiver operating characteristic(ROC) curve(AUC).And the optimal model was interpreted using the SHAP method.

Results

The incidence of preoperative frailty in breast cancer patients was 26.93%.Compared to the support vector machine,decision tree,and light gradient boosting machine models,the extreme gradient boosting model demonstrated the best performance,with an AUC of 0.909,accuracy of 0.829,precision of 0.646,sensitivity of 0.857,specificity of 0.818, and an F1-score of 0.737.The SHAP bar plot identified the top five influencing factors as age,hemoglobin,albumin,neutrophil percentage,and comorbidities.

Conclusions

The extreme gradient boosting model exhibits the best predictive performance and can serve as a reliable tool for healthcare providers to effectively assess and scientifically manage preoperative frailty in breast cancer patients.

breast cancer  /  frailty  /  preoperative  /  machine learning  /  prediction model  /  influencing factors  /  comorbidities
张晴, 张英, 董建丽, 于文龙, 熊银环, 赵佳月, 许红梅. 基于可解释机器学习算法构建乳腺癌病人术前衰弱风险预测模型. 护理研究, 2026 , 40 (1) : 60 -70 . DOI: 10.12102/j.issn.1009-6493.2026.01.008
Qing ZHANG, Ying ZHANG, Jianli DONG, Wenlong YU, Yinhuan XIONG, Jiayue ZHAO, Hongmei XU. Construction of a prediction model for the preoperative frailty risk of breast cancer patients based on interpretable machine learning algorithms[J]. Chinese Nursing Research, 2026 , 40 (1) : 60 -70 . DOI: 10.12102/j.issn.1009-6493.2026.01.008
Year 2026 volume 40 Issue 1
44
14
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Article Info
doi: 10.12102/j.issn.1009-6493.2026.01.008
  • Receive Date:2025-03-24
  • Online Date:2026-10-28
  • Published:2026-01-10
Article Data
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History
  • Received:2025-03-24
  • Revised:2025-11-15
Affiliations
    1School of Nursing, Binzhou Medical University(School of Gerontology), Shandong 256600 China
    2Binzhou People's Hospital
    3Affiliated Hospital of Binzhou Medical University

Corresponding:

XU Hongmei, E-mail:
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表12种不同金属材料的力学参数
科
Family
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Number of
genus
种数
Number of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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