ArticleFrontiers in oncology2023
Applications of different machine learning approaches in prediction of breast cancer diagnosis delay.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Deep meta-learning framework with U-Net segmentation and optimization-based feature selection for breast cancer detection in MRI images.Quantitative imaging in medicine and surgery · 2026Article
- An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- Value of an automated machine learning model with post-hoc explanation for predicting healthcare-seeking delays among residents in Tibetan regions.Frontiers in public health · 2026Article
- Article
- Development and validation of a machine learning model to predict delays in seeking medical care among patients with breast cancer in China.BMC cancer · 2025Article
- ViT-DCNN: Vision Transformer with Deformable CNN Model for Lung and Colon Cancer Detection.Cancers · 2025Article
- Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis.Scientific reports · 2025Article
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Machine learning based models for predicting presentation delay risk among gastric cancer patients.Frontiers in oncology · 2024Article
- Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning.PLOS digital health · 2023Article
- Article
- Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study.Frontiers in oncology · 2023Article
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7 authors.
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Abstract
Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran. Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey. Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis. Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis.
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