Article(id=1322165000442065121, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322164984558243894, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.01.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742745600000, receivedDateStr=2025-03-24, revisedDate=1763136000000, revisedDateStr=2025-11-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1793158300742, onlineDateStr=2026-10-28, pubDate=1767974400000, pubDateStr=2026-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793158300742, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793158300742, creator=13701087609, updateTime=1793158300742, updator=13701087609, issue=Issue{id=1322164984558243894, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='1', pageStart='1', pageEnd='176', issueExtLink='null', onlineDate='null', pubDate='1767974400000', pubDateStr='2026-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793158296955, creator='13701087609', updateTime=1793158296955, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=60, endPage=70, ext={EN=ArticleExt(id=1322165000676946146, articleId=1322165000442065121, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction of a prediction model for the preoperative frailty risk of breast cancer patients based on interpretable machine learning algorithms, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
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.

, authors=Qing ZHANG1, Ying ZHANG2, Jianli DONG3, Wenlong YU2, Yinhuan XIONG2, Jiayue ZHAO1, Hongmei XU1, authorsList=Qing ZHANG, Ying ZHANG, Jianli DONG, Wenlong YU, Yinhuan XIONG, Jiayue ZHAO, Hongmei XU, authorCompany=null, correspAuthors=Hongmei XU, authorNote=null, correspAuthorsNote=
XU Hongmei, E-mail:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322165003919143164, articleId=1322165000442065121, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=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, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

分析乳腺癌病人术前衰弱的影响因素并构建术前衰弱风险预测模型。

方法

选取2024年8月—2025年1月在山东省滨州市某2所三级甲等医院乳腺外科拟行手术治疗的住院病人583例,按照7∶3的比例随机分为训练集408例和验证集175例,基于LASSO回归和Boruta算法共同筛选变量,利用支持向量机、决策树、轻量梯度提升和极端梯度提升4种机器学习算法分别构建预测模型,采用准确度、精确度、敏感度、特异度、F1值和受试者工作特征(ROC)曲线下面积(AUC)比较各模型性能。基于Shaply加性解释(SHAP)算法对最优模型进行解释。

结果

乳腺癌病人术前衰弱的发生率为26.93%。与支持向量机、决策树、轻量梯度提升机模型比较,极端梯度提升模型性能最优,其AUC为0.909,准确度为0.829,精确度为0.646,敏感度为0.857,特异度为0.818,F1值为0.737。SHAP条形图显示,排名前5位的影响因素为年龄、血红蛋白、白蛋白、中性粒细胞百分比和合并症。

结论

极端梯度提升模型预测乳腺癌病人术前衰弱风险性能最佳,可为临床工作者有效评估和科学管理乳腺癌术前衰弱病人提供依据。

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张晴,护师,硕士研究生在读

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许红梅,E-mail:
XU Hongmei, E-mail:
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tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322165000442065121, doi=null, pmid=null, pmcid=null, year=2023, volume=179, issue=null, pageStart=112253, pageEnd=null, url=null, language=null, rfNumber=33, rfOrder=32, authorNames=ZHANG L Y, ZENG X F, HE F Q, journalName=Experimental Gerontology, refType=null, unstructuredReference=ZHANG L Y,ZENG X F,HE F Q,et al.Inflammatory biomarkers of frailty:a review[J].Experimental Gerontology,2023,179:112253., articleTitle=Inflammatory biomarkers of frailty:a review, refAbstract=null), Reference(id=1322529744684437974, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322165000442065121, doi=null, pmid=null, pmcid=null, year=2024, volume=16, issue=4, pageStart=2482, pageEnd=2498, url=null, language=null, rfNumber=34, rfOrder=33, authorNames=CHEN Y, YU Y L, YANG D M, journalName=Journal of Thoracic Disease, refType=null, unstructuredReference=CHEN Y,YU Y L,YANG D M,et al.Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease[J].Journal of Thoracic Disease,2024,16(4):2482-2498., articleTitle=Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease, refAbstract=null), Reference(id=1322529744768324055, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322165000442065121, doi=null, pmid=null, pmcid=null, year=2019, volume=2, issue=7, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=35, rfOrder=34, authorNames=MORTAZAVI B J, BUCHOLZ E M, DESAI N R, journalName=JAMA Network Open, refType=null, unstructuredReference=MORTAZAVI B J,BUCHOLZ E M,DESAI N R,et al.Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention[J].JAMA Network Open,2019,2(7):e196835., 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(绿色为Boruta算法确认的变量,红色为拒绝的变量)

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Univariate analysis of factors influencing preoperative frailty in breast cancer patients

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类非衰弱(n=426)衰弱(n=157)统计值P
年龄[M(P25,P75),岁]55.00(47.00,60.00)62.00(55.00,69.00)Z=-8.001<0.001
BMI[M(P25,P75),kg/m2]24.59(22.89,26.00)24.67(22.78,25.89)Z=-0.2480.804
臂围[M(P25,P75),cm]27.00(25.00,28.00)26.00(25.00,28.00)Z=-2.5260.012
小腿围[M(P25,P75),cm]35.00(34.00,36.00)35.00(33.00,36.00)Z=-0.5150.607
臀围[M(P25,P75),cm]100.00(95.25,103.00)97.00(93.00,102.00)Z=-3.175<0.001
文化程度[例(%)]小学及以下227(53.29)111(70.70)χ2=17.094<0.001
初中104(24.41)28(17.83)
高中或中专48(11.27)13(8.28)
专科及以上47(11.03)5(3.18)
婚姻状况[例(%)]已婚395(92.72)136(86.62)χ2=5.2530.022
非婚(未婚/离异/丧偶)31(7.28)21(13.38)
个人月收入[例(%)]<3 000元237(55.63)110(70.06)
3 000~6 000元126(29.58)31(19.75)χ2=9.9210.007
>6 000元63(14.79)16(10.19)
吸烟史[例(%)]否404(94.84)140(89.17)χ2=5.8950.015
是22(5.16)17(10.83)
饮酒史[例(%)]否394(92.49)147(93.63)χ2=0.2240.636
是32(7.51)10(6.37)
跌倒史[例(%)]否392(92.02)130(82.80)χ2=10.401<0.001
是34(7.98)27(17.20)
独居[例(%)]否403(94.60)136(86.62)χ2=10.461<0.001
是23(5.40)21(13.38)
睡眠[例(%)]正常300(70.42)93(59.24)χ2=0.0110.535
困难126(29.58)64(40.76)
付费方式[例(%)]新型农村合作医疗229(53.76)85(54.14)
城镇居民医保59(13.85)33(21.02)χ2=5.8960.052
离休/职工医保138(32.39)39(24.84)
锻炼情况[例(%)]无规律锻炼255(59.86)109(69.43)χ2=7.4720.006
规律锻炼171(40.14)48(30.57)
合并症[例(%)]≤1种192(45.07)39(24.84)χ2=19.625<0.001
≥2种234(54.93)118(75.16)
多重用药[例(%)]否384(90.14)119(75.80)χ2=19.939<0.001
是42(9.86)38(24.20)
自理[例(%)]独立407(95.54)112(71.34)χ2=68.701<0.001
非独立19(4.46)45(28.66)
营养状况[例(%)]正常353(82.86)102(64.97)χ2=21.441<0.001
存在风险/营养不良73(17.14)55(35.03)
焦虑[例(%)]否326(76.53)104(66.24)χ2=6.2680.012
是100(23.47)53(33.76)
抑郁[例(%)]否335(78.64)109(69.43)χ2=5.3610.021
是91(21.36)48(30.57)
社会支持[M(P25,P75),分]44.00(40.00,48.00)41.00(37.00,46.00)Z=-5.219<0.001
病理分型[例(%)]非浸润性83(19.48)24(15.29)χ2=1.3490.246
浸润性343(80.52)133(84.71)
TNM分期[例(%)]Ⅰ期117(27.46)28(17.83)
Ⅱ期249(58.45)88(56.05)χ2=13.976<0.001
Ⅲ期/Ⅳ期60(14.08)41(26.11)
术前化疗[例(%)]否333(78.17)104(66.24)χ2=8.6940.003
是93(21.83)53(33.76)
血红蛋白[M(P25,P75),g/L]129.00(120.00,136.00)116.00(108.50,127.00)Z=-8.592<0.001
白细胞计数[M(P25,P75),×109/L]4.70(4.20,5.40)4.70(4.24,5.21)Z=-0.6720.502
红细胞计数[M(P25,P75),×1012/L]4.25(3.80,4.60)3.83(3.60,4.20)Z=-6.055<0.001
中性粒细胞百分比[M(P25,P75),%]56.98(53.08,62.32)61.10(56.90,66.26)Z=-5.736<0.001
淋巴细胞绝对值[M(P25,P75),×109/L]1.90(1.60,2.20)1.90(1.70,2.30)Z=-1.4050.160
白蛋白[M(P25,P75),g/L]42.50(39.62,44.41)39.50(36.90,43.26)Z=-6.155<0.001
总蛋白[M(P25,P75),g/L]69.80(66.90,74.47)70.10(66.92,74.60)Z=-0.0570.955
低密度脂蛋白胆固醇[M(P25,P75),mmol/L]2.58(2.34,2.96)2.63(1.98,2.96)Z=-0.9530.341
), ArticleFig(id=1322529736618791344, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322165000442065121, language=CN, label=表1, caption=

乳腺癌病人术前衰弱影响因素的单因素分析

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变量分类非衰弱(n=426)衰弱(n=157)统计值P
年龄[M(P25,P75),岁]55.00(47.00,60.00)62.00(55.00,69.00)Z=-8.001<0.001
BMI[M(P25,P75),kg/m2]24.59(22.89,26.00)24.67(22.78,25.89)Z=-0.2480.804
臂围[M(P25,P75),cm]27.00(25.00,28.00)26.00(25.00,28.00)Z=-2.5260.012
小腿围[M(P25,P75),cm]35.00(34.00,36.00)35.00(33.00,36.00)Z=-0.5150.607
臀围[M(P25,P75),cm]100.00(95.25,103.00)97.00(93.00,102.00)Z=-3.175<0.001
文化程度[例(%)]小学及以下227(53.29)111(70.70)χ2=17.094<0.001
初中104(24.41)28(17.83)
高中或中专48(11.27)13(8.28)
专科及以上47(11.03)5(3.18)
婚姻状况[例(%)]已婚395(92.72)136(86.62)χ2=5.2530.022
非婚(未婚/离异/丧偶)31(7.28)21(13.38)
个人月收入[例(%)]<3 000元237(55.63)110(70.06)
3 000~6 000元126(29.58)31(19.75)χ2=9.9210.007
>6 000元63(14.79)16(10.19)
吸烟史[例(%)]否404(94.84)140(89.17)χ2=5.8950.015
是22(5.16)17(10.83)
饮酒史[例(%)]否394(92.49)147(93.63)χ2=0.2240.636
是32(7.51)10(6.37)
跌倒史[例(%)]否392(92.02)130(82.80)χ2=10.401<0.001
是34(7.98)27(17.20)
独居[例(%)]否403(94.60)136(86.62)χ2=10.461<0.001
是23(5.40)21(13.38)
睡眠[例(%)]正常300(70.42)93(59.24)χ2=0.0110.535
困难126(29.58)64(40.76)
付费方式[例(%)]新型农村合作医疗229(53.76)85(54.14)
城镇居民医保59(13.85)33(21.02)χ2=5.8960.052
离休/职工医保138(32.39)39(24.84)
锻炼情况[例(%)]无规律锻炼255(59.86)109(69.43)χ2=7.4720.006
规律锻炼171(40.14)48(30.57)
合并症[例(%)]≤1种192(45.07)39(24.84)χ2=19.625<0.001
≥2种234(54.93)118(75.16)
多重用药[例(%)]否384(90.14)119(75.80)χ2=19.939<0.001
是42(9.86)38(24.20)
自理[例(%)]独立407(95.54)112(71.34)χ2=68.701<0.001
非独立19(4.46)45(28.66)
营养状况[例(%)]正常353(82.86)102(64.97)χ2=21.441<0.001
存在风险/营养不良73(17.14)55(35.03)
焦虑[例(%)]否326(76.53)104(66.24)χ2=6.2680.012
是100(23.47)53(33.76)
抑郁[例(%)]否335(78.64)109(69.43)χ2=5.3610.021
是91(21.36)48(30.57)
社会支持[M(P25,P75),分]44.00(40.00,48.00)41.00(37.00,46.00)Z=-5.219<0.001
病理分型[例(%)]非浸润性83(19.48)24(15.29)χ2=1.3490.246
浸润性343(80.52)133(84.71)
TNM分期[例(%)]Ⅰ期117(27.46)28(17.83)
Ⅱ期249(58.45)88(56.05)χ2=13.976<0.001
Ⅲ期/Ⅳ期60(14.08)41(26.11)
术前化疗[例(%)]否333(78.17)104(66.24)χ2=8.6940.003
是93(21.83)53(33.76)
血红蛋白[M(P25,P75),g/L]129.00(120.00,136.00)116.00(108.50,127.00)Z=-8.592<0.001
白细胞计数[M(P25,P75),×109/L]4.70(4.20,5.40)4.70(4.24,5.21)Z=-0.6720.502
红细胞计数[M(P25,P75),×1012/L]4.25(3.80,4.60)3.83(3.60,4.20)Z=-6.055<0.001
中性粒细胞百分比[M(P25,P75),%]56.98(53.08,62.32)61.10(56.90,66.26)Z=-5.736<0.001
淋巴细胞绝对值[M(P25,P75),×109/L]1.90(1.60,2.20)1.90(1.70,2.30)Z=-1.4050.160
白蛋白[M(P25,P75),g/L]42.50(39.62,44.41)39.50(36.90,43.26)Z=-6.155<0.001
总蛋白[M(P25,P75),g/L]69.80(66.90,74.47)70.10(66.92,74.60)Z=-0.0570.955
低密度脂蛋白胆固醇[M(P25,P75),mmol/L]2.58(2.34,2.96)2.63(1.98,2.96)Z=-0.9530.341
), ArticleFig(id=1322529736690094513, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322165000442065121, language=EN, label=Table 2, caption=

Comparison of baseline characteristics between the training set and the validation set

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变量分类训练集(n=408)验证集(n=175)统计值P
衰弱情况[例(%)]非衰弱300(73.53)126(72.00)χ2=0.1460.703
衰弱108(26.47)49(28.00)
年龄[M(P25,P75),岁]56.50(49.00,63.00)56.00(48.00,62.00)Z=-0.9610.337
BMI[M(P25,P75),kg/m2]24.63(22.84,25.89)24.67(22.87,26.37)Z=-0.9120.362
臂围[M(P25,P75),cm]27.00(25.00,28.00)27.00(25.00,28.00)Z=-0.1520.879
小腿围[M(P25,P75),cm]35.00(34.00,36.00)35.00(33.00,36.00)Z=-1.0440.297
臀围[M(P25,P75),cm]99.00(95.00,103.00)99.00(94.00,103.00)Z=-0.0840.933
文化程度[例(%)]小学及以下239(58.58)99(56.57)χ2=5.2460.155
初中99(24.26)33(18.86)
高中或中专37(9.07)24(13.71)
专科及以上33(8.09)19(10.86)
婚姻状况[例(%)]已婚369(90.44)162(92.57)χ2=0.6840.408
非婚(未婚/离异/丧偶)39(9.56)13(7.43)
个人月收入[例(%)]<3 000元243(59.56)104(59.43)
3 000~6 000元110(26.96)47(26.86)χ2=0.0060.997
>6 000元55(13.48)24(13.71)
吸烟史[例(%)]否376(92.16)168(96.00)χ2=2.8980.089
是32(7.84)7(4.00)
饮酒史[例(%)]否378(92.65)163(93.14)χ2=0.0450.832
是30(7.35)12(6.86)
跌倒史[例(%)]否364(89.22)158(90.29)χ2=0.1500.699
是44(10.78)17(9.71)
独居[例(%)]否378(92.65)161(92.00)χ2=0.0730.786
是30(7.35)14(8.00)
睡眠[例(%)]正常269(65.93)124(70.86)χ2=1.3530.245
困难139(34.07)51(29.14)
付费方式[例(%)]新型农村合作医疗219(53.68)95(54.29)
城镇居民医保70(17.16)22(12.57)χ2=2.2780.320
离休/职工医保119(29.17)58(33.14)
锻炼情况[例(%)]无规律锻炼260(63.73)104(59.43)χ2=0.9640.326
规律锻炼148(36.27)71(40.57)
合并症[例(%)]≤1种159(38.97)72(41.14)χ2=0.2430.623
≥2种249(61.03)103(58.86)
多重用药[例(%)]否353(86.52)150(85.71)χ2=0.0670.796
是55(13.48)25(14.29)
自理[例(%)]独立360(88.24)159(90.86)χ2=0.8610.353
非独立48(11.76)16(9.14)
营养状况[例(%)]正常315(77.21)140(80.00)χ2=0.5580.455
存在风险/营养不良93(22.79)35(20.00)
焦虑[例(%)]否307(75.25)123(70.29)χ2=1.5560.212
是101(24.75)52(29.71)
抑郁[例(%)]否309(75.74)135(77.14)χ2=0.1340.715
是99(24.26)40(22.86)
社会支持[M(P25,P75),分]44.00(39.00,47.00)44.00(39.00,48.00)Z=-0.3750.708
病理分型[例(%)]非浸润性74(18.14)33(18.86)χ2=0.0420.837
浸润性334(81.86)142(81.14)
TNM分期[例(%)]Ⅰ期105(25.74)40(22.86)
Ⅱ期231(56.62)106(60.57)χ2=0.8210.663
Ⅲ期/Ⅳ期72(17.65)29(16.57)
术前化疗[例(%)]否302(74.02)135(77.14)χ2=0.6360.425
是106(25.98)40(22.86)
血红蛋白[M(P25,P75),g/L]127.00(116.00,133.75)125.00(115.50,134.00)Z=-0.4260.670
白细胞计数[M(P25,P75),×109/L]4.70(4.20,5.36)4.70(4.30,5.30)Z=-0.3830.701
红细胞计数[M(P25,P75),×1012/L]4.20(3.70,4.60)4.20(3.70,4.50)Z=-0.3920.695
中性粒细胞百分比[M(P25,P75),%]57.83(53.90,63.60)58.63(55.23,64.10)Z=-0.5900.555
淋巴细胞绝对值[M(P25,P75),×109/L]1.90(1.60,2.20)1.90(1.60,2.20)Z=-0.2300.818
白蛋白[M(P25,P75),g/L]42.10(38.96,44.02)42.26(39.40,44.50)Z=-1.1180.264
总蛋白[M(P25,P75),g/L]69.87(66.80,74.40)69.60(67.40,74.60)Z=-0.4400.660
低密度脂蛋白胆固醇[M(P25,P75),mmol/L]2.58(2.15,2.98)2.63(2.34,2.96)Z=-0.3850.700
), ArticleFig(id=1322529736761397682, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322165000442065121, language=CN, label=表2, caption=

训练集与验证集基线资料比较

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变量分类训练集(n=408)验证集(n=175)统计值P
衰弱情况[例(%)]非衰弱300(73.53)126(72.00)χ2=0.1460.703
衰弱108(26.47)49(28.00)
年龄[M(P25,P75),岁]56.50(49.00,63.00)56.00(48.00,62.00)Z=-0.9610.337
BMI[M(P25,P75),kg/m2]24.63(22.84,25.89)24.67(22.87,26.37)Z=-0.9120.362
臂围[M(P25,P75),cm]27.00(25.00,28.00)27.00(25.00,28.00)Z=-0.1520.879
小腿围[M(P25,P75),cm]35.00(34.00,36.00)35.00(33.00,36.00)Z=-1.0440.297
臀围[M(P25,P75),cm]99.00(95.00,103.00)99.00(94.00,103.00)Z=-0.0840.933
文化程度[例(%)]小学及以下239(58.58)99(56.57)χ2=5.2460.155
初中99(24.26)33(18.86)
高中或中专37(9.07)24(13.71)
专科及以上33(8.09)19(10.86)
婚姻状况[例(%)]已婚369(90.44)162(92.57)χ2=0.6840.408
非婚(未婚/离异/丧偶)39(9.56)13(7.43)
个人月收入[例(%)]<3 000元243(59.56)104(59.43)
3 000~6 000元110(26.96)47(26.86)χ2=0.0060.997
>6 000元55(13.48)24(13.71)
吸烟史[例(%)]否376(92.16)168(96.00)χ2=2.8980.089
是32(7.84)7(4.00)
饮酒史[例(%)]否378(92.65)163(93.14)χ2=0.0450.832
是30(7.35)12(6.86)
跌倒史[例(%)]否364(89.22)158(90.29)χ2=0.1500.699
是44(10.78)17(9.71)
独居[例(%)]否378(92.65)161(92.00)χ2=0.0730.786
是30(7.35)14(8.00)
睡眠[例(%)]正常269(65.93)124(70.86)χ2=1.3530.245
困难139(34.07)51(29.14)
付费方式[例(%)]新型农村合作医疗219(53.68)95(54.29)
城镇居民医保70(17.16)22(12.57)χ2=2.2780.320
离休/职工医保119(29.17)58(33.14)
锻炼情况[例(%)]无规律锻炼260(63.73)104(59.43)χ2=0.9640.326
规律锻炼148(36.27)71(40.57)
合并症[例(%)]≤1种159(38.97)72(41.14)χ2=0.2430.623
≥2种249(61.03)103(58.86)
多重用药[例(%)]否353(86.52)150(85.71)χ2=0.0670.796
是55(13.48)25(14.29)
自理[例(%)]独立360(88.24)159(90.86)χ2=0.8610.353
非独立48(11.76)16(9.14)
营养状况[例(%)]正常315(77.21)140(80.00)χ2=0.5580.455
存在风险/营养不良93(22.79)35(20.00)
焦虑[例(%)]否307(75.25)123(70.29)χ2=1.5560.212
是101(24.75)52(29.71)
抑郁[例(%)]否309(75.74)135(77.14)χ2=0.1340.715
是99(24.26)40(22.86)
社会支持[M(P25,P75),分]44.00(39.00,47.00)44.00(39.00,48.00)Z=-0.3750.708
病理分型[例(%)]非浸润性74(18.14)33(18.86)χ2=0.0420.837
浸润性334(81.86)142(81.14)
TNM分期[例(%)]Ⅰ期105(25.74)40(22.86)
Ⅱ期231(56.62)106(60.57)χ2=0.8210.663
Ⅲ期/Ⅳ期72(17.65)29(16.57)
术前化疗[例(%)]否302(74.02)135(77.14)χ2=0.6360.425
是106(25.98)40(22.86)
血红蛋白[M(P25,P75),g/L]127.00(116.00,133.75)125.00(115.50,134.00)Z=-0.4260.670
白细胞计数[M(P25,P75),×109/L]4.70(4.20,5.36)4.70(4.30,5.30)Z=-0.3830.701
红细胞计数[M(P25,P75),×1012/L]4.20(3.70,4.60)4.20(3.70,4.50)Z=-0.3920.695
中性粒细胞百分比[M(P25,P75),%]57.83(53.90,63.60)58.63(55.23,64.10)Z=-0.5900.555
淋巴细胞绝对值[M(P25,P75),×109/L]1.90(1.60,2.20)1.90(1.60,2.20)Z=-0.2300.818
白蛋白[M(P25,P75),g/L]42.10(38.96,44.02)42.26(39.40,44.50)Z=-1.1180.264
总蛋白[M(P25,P75),g/L]69.87(66.80,74.40)69.60(67.40,74.60)Z=-0.4400.660
低密度脂蛋白胆固醇[M(P25,P75),mmol/L]2.58(2.15,2.98)2.63(2.34,2.96)Z=-0.3850.700
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Performance evaluation of the four machine learning models in the training set and validation set

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项目模型准确度精确度敏感度特异度F1值AUC
训练集支持向量机0.8600.7220.7690.8930.7440.895
决策树0.8970.6940.8930.8980.7810.924
轻量梯度提升机0.8380.6720.7590.8670.7130.883
极端梯度提升机0.8780.7040.9260.8600.8000.954
验证集支持向量机0.8340.7170.6740.8970.6950.878
决策树0.8290.4900.8280.8290.6150.841
轻量梯度提升机0.8400.7140.7140.8890.7140.886
极端梯度提升机0.8290.6460.8570.8180.7370.909
), ArticleFig(id=1322529738552365492, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322165000442065121, language=CN, label=表3, caption=

4种机器学习模型在训练集和验证集的性能评价

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项目模型准确度精确度敏感度特异度F1值AUC
训练集支持向量机0.8600.7220.7690.8930.7440.895
决策树0.8970.6940.8930.8980.7810.924
轻量梯度提升机0.8380.6720.7590.8670.7130.883
极端梯度提升机0.8780.7040.9260.8600.8000.954
验证集支持向量机0.8340.7170.6740.8970.6950.878
决策树0.8290.4900.8280.8290.6150.841
轻量梯度提升机0.8400.7140.7140.8890.7140.886
极端梯度提升机0.8290.6460.8570.8180.7370.909
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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
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张晴 1 , 张英 2 , 董建丽 3 , 于文龙 2 , 熊银环 2 , 赵佳月 1 , 许红梅 1, *, *
护理研究 | 科研论著 2026,40(1): 60-70
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护理研究 |科研论著 2026 , 40 (1) : 60 -70
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
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张晴1, 张英2, 董建丽3, 于文龙2, 熊银环2, 赵佳月1, 许红梅1, *, *
作者信息
  • 1滨州医学院护理学院(老年医学院),山东 256600
  • 2滨州市人民医院
  • 3滨州医学院附属医院
通讯作者:
许红梅,E-mail:
XU Hongmei, E-mail:
作者简介:

张晴,护师,硕士研究生在读

Construction of a prediction model for the preoperative frailty risk of breast cancer patients based on interpretable machine learning algorithms
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
  • Corresponding Author:
    XU Hongmei, E-mail:
出版时间: 2026-01-10 doi: 10.12102/j.issn.1009-6493.2026.01.008
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目的

分析乳腺癌病人术前衰弱的影响因素并构建术前衰弱风险预测模型。

方法

选取2024年8月—2025年1月在山东省滨州市某2所三级甲等医院乳腺外科拟行手术治疗的住院病人583例,按照7∶3的比例随机分为训练集408例和验证集175例,基于LASSO回归和Boruta算法共同筛选变量,利用支持向量机、决策树、轻量梯度提升和极端梯度提升4种机器学习算法分别构建预测模型,采用准确度、精确度、敏感度、特异度、F1值和受试者工作特征(ROC)曲线下面积(AUC)比较各模型性能。基于Shaply加性解释(SHAP)算法对最优模型进行解释。

结果

乳腺癌病人术前衰弱的发生率为26.93%。与支持向量机、决策树、轻量梯度提升机模型比较,极端梯度提升模型性能最优,其AUC为0.909,准确度为0.829,精确度为0.646,敏感度为0.857,特异度为0.818,F1值为0.737。SHAP条形图显示,排名前5位的影响因素为年龄、血红蛋白、白蛋白、中性粒细胞百分比和合并症。

结论

极端梯度提升模型预测乳腺癌病人术前衰弱风险性能最佳,可为临床工作者有效评估和科学管理乳腺癌术前衰弱病人提供依据。

乳腺癌  /  衰弱  /  术前  /  机器学习  /  预测模型  /  影响因素  /  合并症
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
乳腺癌是全球女性发病率最高的恶性肿瘤之一,也是女性癌症相关死亡最主要的原因[1]。2022年,我国乳腺癌新发病例约35.7万例,死亡病例约7.5万例,严重危害了女性的身体健康[2]。外科手术是其主要的治疗方式。衰弱是由于生理储备下降、多系统功能失调从而导致机体抗应激能力下降的综合状态[3]。乳腺癌病人由于肿瘤细胞的消耗及治疗相关副作用的影响更容易发生衰弱。研究表明,乳腺癌病人术前衰弱的发生率高达29.3%[4]。术前衰弱会导致术后并发症及住院时间延长、生活质量下降、死亡风险增加等一系列不良后果[5-7]。术前早期识别及评估衰弱状态十分必要。风险预测模型通过向多个预测因子赋予相对权重,预测个体某种特定结果存在的概率或风险。目前,已有研究通过构建风险预测模型用于有效评估或预防衰弱,但研究对象多集中于糖尿病、冠心病及肺癌等人群[8-10]。针对乳腺癌术前病人的研究较少。因此,探究乳腺癌术前病人衰弱的影响因素并构建风险预测模型具有重要意义。
近年来,随着数字技术的不断发展,机器学习算法逐渐步入医学领域。与传统算法相比,机器学习算法可以更高效地处理高维大规模数据并捕捉数据间非线性关系,从而更准确地预测疾病的发生[11]。但由于建模过程的复杂化导致模型缺乏透明度和可解释性,因此被称为“黑箱模型”。Shaply加性解释(Shapley additive explanations,SHAP)可以分析每个特征与预测结局之间的量效关系,并从单样本和全局两方面进行解释,提高模型的透明度。因此,本研究利用4种机器学习算法构建乳腺癌病人术前衰弱风险预测模型,并基于SHAP算法对最优模型进行解释,以期为临床工作者术前早期识别并有效预防衰弱提供帮助。
采用便利抽样法,选取2024年8月—2025年1月在山东省滨州市某2所三级甲等医院乳腺外科拟行手术治疗的乳腺癌住院病人为研究对象。纳入标准:1)符合“中国抗癌协会乳腺癌诊治指南与规范(2024年版)”[12]诊断标准,经病理诊断确诊为乳腺癌且拟行外科手术治疗;2)年龄≥18岁的女性;3)知情同意参加本研究。排除标准:1)有交流沟通障碍者;2)痴呆或严重精神疾病者。本研究根据每变量事件数(event per variable,EPV)原则进行样本量计算:EPV取值为10,预计纳入10个预测变量,文献报道乳腺癌术前衰弱的发生率为29.3%[4],考虑10%的无效样本,所需样本量约为375例。本研究已获得滨州医学院伦理委员会批准(批准号:2024-L038)。
基于本研究目的,回顾相关文献并结合临床经验和专家意见收集资料。1)一般资料:包括年龄、体质指数(BMI)、臂围、小腿围、臀围、文化程度、婚姻状况、个人月收入、吸烟史、饮酒史、跌倒史、独居、睡眠、付费方式、锻炼情况。2)疾病相关资料及术前实验室检查指标:包括合并症、多重用药、病理分型、TNM分期、术前化疗、血红蛋白、红细胞计数、白细胞计数、中性粒细胞百分比、淋巴细胞绝对值、白蛋白、总蛋白和低密度脂蛋白胆固醇。3)Barthel指数:由Mahoney等[13]编制,常用于评估日常生活活动能力(ADL),共10个条目,满分100分,<100分代表ADL非独立,该量表的Cronbach′s α系数为0.94。4)微型营养评定简表(MAN-SF):该量表由Rubenstein等[14]在微型营养评定量表(MAN)的基础上简化而来,共6个条目,总分0~14分,≤7分为营养不良,8~11分为存在营养不良风险,12~14分为营养正常。该量表检测敏感度为96%[15]。5)医院焦虑抑郁量表(HADS):该量表由Zigmond等[16]编制,包含焦虑和抑郁两个子量表,每个子量表包含7个条目,得分为0~21分。≥8分为焦虑或抑郁。该量表的Cronbach′s α系数分别为0.820,0.807[17]。6)社会支持评定量表(SSRS):该量表由肖水源[18]编制,包含3个维度、10个条目,总分为12~66分,得分越高代表社会支持水平越高。该量表的Cronbach′s α系数为0.896[19]。7)衰弱:采用FRAIL量表评估,量表由国际营养与老年协会专家组提出[20],卫尹等[21]汉化,并在老年住院衰弱病人中证实了其具有良好的信效度。该量表包括疲乏、耐力减退、体重减轻、多病共存和行动能力下降5个条目。总分0~5分,0分为无衰弱,1~2分为衰弱前期,≥3分为衰弱。本研究将无衰弱和衰弱前期统称非衰弱。
采取问卷调查和病案系统相结合的方式。由经过统一培训的1名研究者在病人入院当天测量臂围、小腿围和臀围,并指导病人完成一般资料调查表、Barthel指数量表等;另1名研究者在病人术前1 d指导病人完成FRAIL量表。所有量表和问卷当场回收并检查,确保问卷填写的完整性。疾病相关资料及术前实验室检查指标由2名研究者共同从病案系统获取,并交叉核对。研究对象所在的2所医院均使用相同的血常规检验设备(Sysmex XE5000全自动血液分析仪)和生化功能检测设备(Beckman Coulter AU5421全自动生化分析仪),且执行相同的检验流程。
将全数据集(n=583)按照7∶3的原则随机分配为训练集(n=408)和验证集(n=175),在训练集中使用LASSO回归(十折交叉验证)与Boruta算法(迭代300次)2种方法筛选预测变量,取2种方法的交集作为最终的预测变量。将预测变量纳入支持向量机、决策树、轻量梯度提升和极端梯度提升4种算法中构建预测模型,并在验证集中进行测试。采用准确度、精确度、敏感度、特异度、F1分数和受试者工作特征(ROC)曲线下面积(AUC)评价模型性能,AUC值和F1分数作为主要的评价指标。基于SHAP算法对最优模型从全局和单样本两方面进行解释,增加模型的透明度。
使用SPSS 25.0和R4.4.2进行统计分析和建模。删除缺失数据超过20%的变量,使用多重插补处理缺失数据。采用Kolmogorov-Smimovz方法对定量资料进行正态性检验,均不符合正态分布,以中位数(四分位数)[M(P25,P75)]表示,两组比较采用Mann-Whitney秩和检验;定性资料以频数、百分比(%)表示,两组比较采用χ2检验;以P<0.05为差异有统计学意义。
本研究共纳入583例乳腺癌术前病人,发生衰弱157例,衰弱发生率为26.93%。年龄、臂围、臀围、文化程度、个人月收入、合并症、多重用药、血红蛋白、红细胞计数和白蛋白在非衰弱组与衰弱组病人间差异均有统计学意义(P<0.05)。具体见表1。
将583例样本按照7∶3随机分配为训练集(n=408)和验证集(n=175),训练集108例(26.47%)衰弱病人,验证集49例(28.00%)衰弱病人。两组衰弱变量及其他变量均平衡可比。具体见表2。
将33个预测变量全部纳入LASSO回归并进行十折交叉验证后,筛选出年龄、合并症、多重用药、自理能力、营养状况、社会支持得分、血红蛋白、红细胞、中性粒细胞百分比、淋巴细胞绝对值和白蛋白11个预测变量,具体见图1和图2。33个预测变量中,Boruta算法拒绝18个变量(红色),0个变量被认为暂定(黄色),接受15个变量(绿色),接受的15个变量分别是:年龄、臀围、文化程度、个人月收入、合并症、多重用药、自理能力、营养状况、社会支持得分、血红蛋白、白细胞、红细胞、中性粒细胞百分比、白蛋白和总蛋白,具体见图3。2种算法取交集,最终筛选出年龄、合并症、多重用药、自理能力、营养状况、社会支持得分、血红蛋白、红细胞、中性粒细胞百分比和白蛋白10个预测变量,具体见图4。
将交集最终筛选出的年龄、合并症、多重用药、自理能力、营养状况、社会支持得分、血红蛋白、红细胞、中性粒细胞百分比和白蛋白10个预测因子作为输入变量,以是否发生衰弱作为结局变量,利用4种机器学习算法分别构建预测模型,并在验证集中进行验证。结果显示,极端梯度提升模型的性能最好,F1值(0.737)、AUC(0.909)和敏感度(0.857)均高于其他模型。各模型的性能评价见表3。分别绘制训练集及验证集的ROC曲线,可以直观看出极端梯度提升模型判别能力最强。具体见图5和图6。
年龄、合并症、多重用药、自理能力、营养状况、社会支持得分、血红蛋白、红细胞计数、中性粒细胞百分比和白蛋白10个预测变量重要性从高到低的排序见图7,排名前5位的变量依次为:年龄、血红蛋白、白蛋白、中性粒细胞百分比和合并症。各预测变量与衰弱的正负性关系结果显示,年龄、中性粒细胞百分比与衰弱风险呈正相关(P<0.05),血红蛋白、白蛋白、红细胞计数、社会支持得分与衰弱风险呈负相关(P<0.05);另外,合并症≥2种、自理能力非独立、营养不良及多重用药的病人发生衰弱风险较大。见图8。
每个变量对衰弱高风险病人和衰弱低风险病人的贡献见图9和图10。具体而言,年龄66岁、社会支持得分38分、合并症≥2种使病人衰弱风险增加(P<0.05);年龄42岁、中性粒细胞百分比52.4%、白蛋白39.5 g/L、红细胞计数4.5×1012/L、独立、社会支持得分43分使病人衰弱风险降低(P<0.05)。
本研究共纳入583例乳腺癌病人,其中157例发生衰弱,衰弱发生率为26.93%,略低于杜诗园[22]研究结果(30.6%)。可能是由于杜诗园[22]使用衰弱指数-28(FI-28)量表进行衰弱的判定,且纳入的研究对象包括乳腺癌术后病人。手术的强大应激、术后疼痛的刺激及身体形象的紊乱均有可能增加衰弱的发生风险[23]。另外,目前衰弱的评估工具存在异质性,尚缺乏最适用于乳腺癌病人衰弱的筛查工具,这也是未来的研究方向[24]。总而言之,乳腺癌病人术前衰弱发生率较高,临床工作者应及早预测衰弱的发生并采取恰当的干预措施延缓或逆转衰弱,从而降低乳腺癌病人术后并发症发生风险,提高病人的生活质量。
通过变量重要性排序可以看出,乳腺癌病人术前衰弱前5位的影响因素为年龄、血红蛋白、白蛋白、中性粒细胞百分比和合并症,与相关研究结果[25]一致。年龄是乳腺癌病人发生衰弱的重要影响因素。Wang等[26]研究发现,与年龄<35岁的乳腺癌病人相比,50~64岁病人的虚弱发生率增加了约7倍,65~69岁病人增加了近10倍,70岁以上病人增加了17倍。随着年龄增加,乳腺癌病人生理储备能力和抗应激能力降低,以及术前心理适应能力较差从而更容易发生衰弱。另外,已有研究证明,老年病人肠道菌群代谢物——短链脂肪酸生成减少,肌肉内脂肪酸沉积导致肌肉质量和力量降低,其是衰弱发生的关键因素[27]。临床工作者应将老年乳腺癌病人作为术前衰弱筛查的重点对象,对其进行科学预防与有效干预,从而改善衰弱状态。还有研究表明,血红蛋白每增加1 g/L,衰弱风险降低4%[28]。血红蛋白是诊断贫血的关键指标,其主要功能为携带氧气从肺脏运送到机体各处。当血红蛋白浓度降低时,机体组织呈现缺氧状态,导致机体易疲劳、认知功能减退,活动耐力下降,从而促进衰弱发生。白蛋白水平较低的病人,衰弱风险增加,白蛋白是衰弱和肌少症共有的生物标志物[29]。白蛋白水平较低反映蛋白质摄入不足或吸收障碍,导致肌肉蛋白质合成不足,肌肉的质量、力量及功能减退,直接促进衰弱的发生。白蛋白水平较低提示乳腺癌病人营养状态欠佳,机体免疫力下降导致发生衰弱。乳腺癌病人由于肿瘤细胞的侵袭及治疗相关的毒副作用更容易导致低蛋白血症,临床医护人员应及时监测白蛋白水平并给予恰当的营养干预。相关专家共识[30]建议稳定期的恶性肿瘤病人每天蛋白质摄入量为1.2~1.5 g/kg,肾功能正常的病人每天蛋白质摄入量可提高至2.0 g/kg。Gilmore等[31]研究发现,接受化疗的乳腺癌病人中性粒细胞数目与衰弱的发生呈正相关(P<0.05)。张思苑等[32]研究表明,包含中性粒细胞在内的泛免疫炎症值(PIV)对术前衰弱有重要的预测价值(AUC=0.815)。本研究发现,中性粒细胞百分比与衰弱风险呈正相关,与既往研究结论一致。中性粒细胞作为机体重要的免疫细胞,其占比升高是身体慢性炎症的信号,而慢性低度炎症通过“代谢-免疫-神经内分泌”网络的干扰,导致多器官功能衰退,最终表现为衰弱[33]。本研究结果显示,合并症≥2种的乳腺癌病人更容易发生衰弱。以往研究表明,与无合并症的病人相比,存在1种或2种合并症的乳腺癌病人衰弱风险增加了3.69倍[26]。多病共存不仅容易引发机体多系统功能紊乱及功能障碍,并且增加了多重用药的风险,药物毒副作用的累积使衰弱发生率增加。此外,衰弱病人机体抵抗力下降也会增加共病的风险,从而造成一系列不良结局的发生。多病共存与衰弱互相促进,形成恶性循环。因此,针对合并症≥2种的乳腺癌病人,更应重视评估衰弱并积极治疗可控疾病,减少衰弱的发生。
本研究通过决策树、支持向量机、轻量梯度提升和极端梯度提升4种机器学习算法构建预测模型,其中极端梯度提升模型的敏感度、F1值和AUC值均高于其他模型,综合比较极端梯度提升机模型性能最优。Chen等[34]使用极端梯度提升、支持向量机等7种算法构建慢性阻塞性肺疾病病人衰弱风险预测模型,通过比较确定极端梯度提升机模型性能最优。这可能是因为极端梯度提升机模型通过对弱预测器的不断迭代而组成强大的集成模型,并且进行了正则化、分位点近似和并行化等技术创新[35],从而使其在精度和实用性上超越了其他模型。基于极端梯度提升算法对乳腺癌病人进行术前衰弱的评估,一方面,可以精准识别衰弱高风险病人,并通过对可控危险因素进行干预,从而降低衰弱发生率,改善病人生活质量;另一方面,可以为后续相关研究提供理论支撑和参考依据,更好地推动乳腺癌病人衰弱的精准预防和科学管理。
本研究从病人一般资料、疾病相关资料及术前实验室检查指标3个方面共提取了33个备选变量,利用LASSO回归和Boruta算法共同筛选出10个重要变量,提高了变量选择的合理性;比较4种预测模型的性能最终得出最优模型,提高了模型的可靠性与准确性,并采用SHAP算法解释了各变量与衰弱之间的关系,便于医护人员和病人直观理解。但仍存在一些局限性:本研究为横断面研究,无法追踪乳腺癌病人衰弱的动态发展情况;仅收集了滨州市2所三级甲等医院的病人资料,代表性局限且未进行外部验证。后续会进一步收集外部验证的数据提高模型的泛化能力。
综上所述,本研究基于LASSO回归和Boruta算法共同筛选出10个影响因素,其中年龄、血红蛋白、白蛋白、中性粒细胞百分比和合并症是排名前5位的影响因素。利用4种机器学习算法构建预测模型,其中极端梯度提升机模型性能最佳。利用SHAP算法对极端梯度提升模型进行解释,解决了“黑箱模型”的弊端,使模型的决策过程更清晰透明。本研究构建的预测模型有助于临床医护人员有效评估乳腺癌病人的术前衰弱状态,为临床医护人员科学、有效地预防和评估衰弱提供依据。

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doi: 10.12102/j.issn.1009-6493.2026.01.008
  • 接收时间:2025-03-24
  • 首发时间:2026-10-28
  • 出版时间:2026-01-10
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  • 收稿日期:2025-03-24
  • 修回日期:2025-11-15
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    1滨州医学院护理学院(老年医学院),山东 256600
    2滨州市人民医院
    3滨州医学院附属医院

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2种不同金属材料的力学参数
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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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