ArticleOncoTargets and therapy2019
Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes.
Article in OncoTargets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses.PloS one · 2026Article
- Chemoimmunomodulation in triple negative breast cancer: a key to maximizing anti-PD-1 chemoimmunotherapeutic efficacy.Oncoimmunology · 2025Review
- Cuproptosis-related risk score based on machine learning algorithm predicts prognosis and characterizes tumor microenvironment in head and neck squamous carcinomas.Scientific reports · 2023Article
- Predictive Significance of Systemic Immune-Inflammation Index in Patients with Breast Cancer: A Retrospective Cohort Study.OncoTargets and therapy · 2023Article
- Investigating the clinical significance of EGFR expression using machine learning in a series of Iraqi patients with triple-negative breast cancer.Journal of medicine and life · 2022Article
- Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a high-volume Chinese center.Frontiers in surgery · 2022Article
- Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning.Journal of personalized medicine · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo use machine learning algorithms to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.
methods1570 stage I-III breast cancer patients receiving treatment from Sun Yat-sen Memorial Hospital were analyzed. Machine learning was used to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.
resultsThe results showed that platelets, LMR (lymphocyte-to-monocyte ratio), age, PLR (the platelet-to-lymphocyte ratio) and white blood cell counts accounted for a significant weight in the 5-year prognosis of triple-negative breast cancer patients. The results of model prediction indicated that rankings for accuracy among the training group (from high to low) were forest, gbm, and DecisionTree (0.770335, 0.760766, 0.751994, 0.737640 and 0.734450, respectively). For AUC value (high to low), they were forest, Logistic and DecisionTree (0.896673, 0.895408, 0.776836, 0.722799 and 0.702804, respectively). The highest MSE value for DecisionTree was 0.2656, and the lowest MSE value for forest was 0.2297. In the test group, accuracy rankings (from high to low) were DecisionTree, and GradientBoosting (0.748408, 0.738854, 0.738854, 0.732484 and gbm, respectively). For AUC value (high to low), the rankings were GradientBoosting, gbm, and DecisionTree (0.731595, 0.715438, 0.712767, 0.708348 and 0.691960, respectively). The maximum MSE value for gbm was 0.2707, and the minimum MSE value for DecisionTree was 0.2516.
conclusionThe machine learning algorithm can predict the death outcomes of patients with triple-negative breast cancer 5 years after discharge. This can be used to estimate individual outcomes for patients with triple-negative breast cancer.
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