ArticleScientific reports2024
Application of machine learning in breast cancer survival prediction using a multimethod approach.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Review
- Breast cancer survival prediction using machine learning and multimodal data for personalized care plan.BMC cancer · 2026Article
- AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework.NPJ genomic medicine · 2026Review
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- An Integrated Statistical and Machine Learning Approach for Breast Cancer Classification Using Tumor Morphological Features.BioMed research international · 2026Article
- Article
- Development and validation of nomograms predicting survival in operable breast cancer patients at reproductive age after breast conserving surgery and postoperative radiotherapy based on SEER database.Translational cancer research · 2025Article
- Medical laboratory data-based models: opportunities, obstacles, and solutions.Journal of translational medicine · 2025Review
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the most prevalent cancers with an increasing trend in both incidence and mortality rates in Iran. Survival analysis is a pivotal measure in setting appropriate care plans. To the best of our knowledge, this study is pioneering in Iran, introducing a multi-method approach using a Deep Neural Network (DNN) and 11 conventional machine learning (ML) methods to predict the 5 year survival of women with breast cancer. Supplying data from two centers comprising a total of 2644 records and incorporating external validation further distinguishes the study. Thirty-four features were selected based on a literature review and common variables in both datasets. Feature selection was also performed using a p value criterion (< 0.05) and a survey involving oncologists. A total of 108 models were trained. According to external validation, the DNN model trained with the Shiraz dataset, considering all features, exhibited the highest accuracy (85.56%). While the DNN model showed superior accuracy in external validation, it did not consistently achieve the highest performance across all evaluation metrics. Notably, models trained with the Shiraz dataset outperformed those trained with the Tehran dataset, possibly due to the lower number of missing values in the Shiraz dataset.
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Registered trials
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.