Detection of breast cancer using machine learning and explainable artificial intelligence
Open accessAbstract
Breast cancer is characterized by the proliferation of abnormal breast cells that eventually turn into malignant tumors. These cancer cells can metastasize to be life-threatening and fatal. An intricate mix of environmental factors and individual genetic composition can lead to the formation of this deadly carcinoma. Improvements in the diagnosis and treatment of cancer are essential given the rising incidence of breast cancer. Over the past few decades, machine learning has helped provide accurate medical diagnosis results. Therefore, this study used diagnostic characteristics of patients and multiple machine learning classifiers to identify breast cancer. Incorporating explainable artificial intelligence techniques revealed the underlying factors for the model predictions, adding a layer of transparency and interpretability. Out of the algorithms, random forest showed the best result, an F1-score of 84%. The stacked ensemble model, which combines the strengths of different models, obtained an F1-score performance of 83%. The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I’m Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.
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The "black box" nature of DL remains a fundamental barrier to clinical trust.
“The “black box” nature of DL remains a fundamental barrier to clinical trust [153,154,155,156,157].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [156]
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Cited by
Sorted by citations
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
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- Authors
- Arravalli, T., Chadaga, K., Muralikrishna, H., Sampathila, N., Cenitta, D., Chadaga, R., & Swathi, K. S.
- Affiliations
- Manipal Academy of Higher Education (IN)
- Corresponding author
- Chadaga, Krishnaraj
- Muralikrishna, H
- Venue
- Scientific Reports
- Published
- 2025-07-24
- Volume
- 15
- Issue
- 1
- Pages
- 26931-26931
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC12290098
- OpenAlex
- W4412637184
- Cited by
- 43
- References
- 30
- Citations by year
- 2026: 34 · 2025: 9
- FWCI
- 33.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Manipal Academy of Higher Education, Manipal
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- breast cancer, breast cancer diagnosis, machine learning classifiers, explainable AI, SHAP, lime, ELI5, anchors, random forest, stacking ensemble, interpretability, clinical decision support
- MeSH terms
- Machine Learning, Adult, Aged, Algorithms, Artificial Intelligence, Breast Neoplasms, Female, Humans, Middle Aged
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