Development and validation of a machine learning model to predict delays in seeking medical care among patients with breast cancer in China.

Xiao Chen, Zhiyan Cheng, Yinfeng Li, Xiaohong Wu, Qing Yang

Abstract readValidation Study

In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

An interpretable machine learning model for predicting sepsis risk in ICU patients with non-traumatic subarachnoid hemorrhage: development and validation using the MIMIC-IV database.Frontiers in neurology
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5 authors.

Xiao Chen *School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Zhiyan Cheng *Nursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of the University of Electronic Science and Technology of China, Chengdu, China.

Yinfeng LiNursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of the University of Electronic Science and Technology of China, Chengdu, China.

Xiaohong WuNursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of the University of Electronic Science and Technology of China, Chengdu, China.

Qing YangNursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of the University of Electronic Science and Technology of China, Chengdu, China. yangqingsc@163.com.

Funding

This work was supported by the National Natural Science Foundation of China 72304060

6 · The paper itself

Abstract

backgroundDelays in seeking medical care may affect the survival rates of breast cancer patients. We aimed to explore potential risk factors for the delay in seeking medical care among breast cancer patients by constructing a highly effective machine learning (ML) prediction model.

methodsA cross-sectional methodology was utilized, and the demographic and clinical characteristics of 540 patients with breast cancer in Sichuan Cancer Hospital from July 2022 to June 2023 were collected to develop a model. Feature selection was performed using a Lasso algorithm, and six ML algorithms, including XGB, LR, RF, CNB, SVM and KNN, were applied for model construction. The k-fold cross-validation method was used for internal verification. And ROC curves, calibration curves, DCA and external validation were used for model evaluation. The SHAP method was used to interpret the model visualization.

resultsA comprehensive analysis was conducted in a cohort of 540 patients diagnosed with breast cancer, of whom 212 patients (39.26%) experienced a delay. The Lasso algorithm selected eight variables that were most suitable for model construction. The RF model demonstrated superior performance compared to the other five prediction models. The AUC values in the training set ROC, validation set ROC, and external verification ROC curves were 1.00, 0.86, and 0.76, respectively in RF model. The results of the calibration curves indicated that the calibration curves of the RF models closely resembled the ideal curves. The DCA curves exhibited a net clinical benefit in comparison to treatment for or treatment for none for all models, with the exception of CNB.

conclusionsThe machine learning algorithm utilized in this study effectively generated a prediction model for delays in seeking medical care for patients with breast cancer. The best RF model’s remarkable predictive power, exhibiting a good discrimination and calibration.

Indexed as

Breast NeoplasmsMachine LearningPatient Acceptance of Health CareAdultAlgorithmsChinaClassification AlgorithmsCross-Sectional StudiesFemaleHumansMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRisk FactorsROC CurveTreatment DelayBreast cancerDelays among patientsMachine learningRisk factorsShapley additive explanations

Identifiers

PMID41029530

DOI10.1186/s12885-025-14813-6

PMCPMC12482459

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.