|Table of Contents|

Research on breast cancer diagnosis model based on clinlabomics and machine learning

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2024 07
Page:
1264-1272
Research Field:
Publishing date:

Info

Title:
Research on breast cancer diagnosis model based on clinlabomics and machine learning
Author(s):
LU Feng1; 2; ZHANG Kaijiong3; WU Lichun3; JIANG Xuchuan2; JI Chengjie2; LIU Jinbo1
1.Medical Laboratory Department of the Affiliated Hospital of Southwest Medical University,Sichuan Luzhou 646000,China;2.Department of Experimental Medicine,Jianyang City People's Hospital,Sichuan Jianyang 641400,China;3.Sichuan Cancer Hospital Institute/Sichuan Cancer Prevention Center/Medical Laboratory Department of the Affiliated Tumor Hospital of University of Electronic Science and Technology,Sichuan Chengdu 610000,China.
Keywords:
breast cancer; machine learning; clinlabomics; diagnostic model; artificial intelligence
PACS:
R737.9
DOI:
10.3969/j.issn.1672-4992.2024.07.014
Abstract:
Objective:To construct a breast cancer diagnostic model using machine learning algorithms based on big data from routine clinical laboratories,in order to explore the application value of clinlabomics in the diagnosis of breast cancer.Methods:A retrospective study was conducted,collecting clinical and laboratory test data from 6 089 breast cancer patients and 6 830 patients with benign breast diseases.Various machine learning algorithms including extreme gradient boosting(XGBoost),neural network(NN),support vector machine(SVM),random forest(RF),K-nearest neighbors(KNN),Logistic regression(LR),linear discriminant analysis(LDA),naive bayes,gradient boosting machine(GBM) algorithm,and C5.0 decision tree were utilized for establishing the breast cancer diagnostic model.Ten-fold cross validation was used to train the model,and the accuracy,AUC,average accuracy,specificity,sensitivity,positive predictive value,negative predictive value,and Kappa value were used to evaluate the performance of each model.Results:Nine indicators were selected from 28 conventional clinical test indicators,including GLU,DBIL,RDW-CV,MONO,TG,ALB,RBC,LYMPH and UREA,and then age was added for model construction.The model was evaluated by using 10 machine learning algorithms,and the results show that the gradient elevator algorithm,has the best diagnostic performance compared with other models.The accuracy,AUC,average accuracy,specificity,sensitivity,positive predictive value,negative predictive value,and Kappa value of gradient elevator algorithm in diagnosing breast cancer were 0.80,0.80,0.80,0.77,0.82,0.78,0.81 and 0.59,respectively.Conclusion:Based on the routine clinical laboratory data,the machine learning algorithm is used to construct the breast cancer diagnosis model,which can provide decision support for the auxiliary diagnosis of breast cancer.

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Memo

Memo:
四川省科技厅重点研发项目第二版(编号：2022YFS0335)；四川省成都市医学科研课题(编号：2022417)；四川省简阳市人民医院科研课题(编号：JY202234)
Last Update: 2024-02-29