ArticleCancers2023
A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Digital Twins for Hospital and Healthcare Operations: A Systematic Review of Resource Allocation, Infection Control, and Workflow Optimization.Bioengineering (Basel, Switzerland) · 2026Review
- Machine Learning and Explainable AI for Breast Cancer Patient Prioritization: An Intelligent Decision-Support Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Genetic analysis of genes GSTP1, PTEN, and TP53 SNPs in non-melanoma skin cancer patients.Journal of cancer research and clinical oncology · 2026Article
- Explainable Mammogram Analysis with EfficientNetV2 and Grad-CAM++ for Robust Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Detection of breast cancer using machine learning and explainable artificial intelligence.Scientific reports · 2025Article
- Enhanced and Interpretable Prediction of Multiple Cancer Types Using a Stacking Ensemble Approach with SHAP Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence.Journal of imaging · 2025Article
- Explainable AI-enabled hybrid deep learning architecture for breast cancer detection.Frontiers in immunology · 2025Article
- Explainable and uncertainty-aware ensemble framework with causal analysis for breast cancer detection.Frontiers in oncology · 2025Article
- XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.Scientific reports · 2024Article
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.Cancer innovation · 2024Article
- Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models.Tropical medicine and infectious disease · 2024Article
- Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.PloS one · 2024Article
- Comparative Analysis of Data Preprocessing Methods in Machine Learning for Breast Cancer Classification.Proceedings. International Conference on Advanced Information Networking and Applications · 2024Article
- A contemporary review of breast cancer risk factors and the role of artificial intelligence.Frontiers in oncology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients' prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.
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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.