Explainable Machine Learning for the Early Clinical Detection of Ovarian Cancer Using Contrastive Explanations

Basit Öğe Kaydı
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorCicek, Ipek Balikci
dc.contributor.authorAkbulut, Sami
dc.date.accessioned2026-04-04T13:31:03Z
dc.date.available2026-04-04T13:31:03Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground: Ovarian cancer is often diagnosed at advanced stages due to the absence of specific early symptoms, resulting in high mortality rates. This study aims to develop a robust and interpretable machine learning (ML) model for the early detection of ovarian cancer, enhancing its transparency through the use of the Contrastive Explanation Method (CEM), an advanced technique within the field of explainable artificial intelligence (XAI). Methods: An open-access dataset of 349 patients with ovarian cancer or benign ovarian tumors was used. To improve reliability, the dataset was augmented via bootstrap resampling. A three-layer deep neural network was trained on normalized demographic, biochemical, and tumor marker features. Model performance was measured using accuracy, sensitivity, specificity, F1-score, and the Matthews correlation coefficient. CEM was used to explain the model's classification results, showing which factors push the model toward Cancer or No Cancer decisions. Results: The model achieved high diagnostic performance, with an accuracy of 95%, sensitivity of 96.2%, and specificity of 93.5%. CEM analysis identified lymphocyte count (CEM value: 1.36), red blood cell count (1.18), plateletcrit (0.036), and platelet count (0.384) as the strongest positive contributors to the Cancer classification, with lymphocyte count demonstrating the highest positive relevance, underscoring its critical role in cancer detection. In contrast, age (change from -0.13 to +0.23) and HE4 (change from -0.43 to -0.05) emerged as key factors in reversing classifications, requiring substantial hypothetical increases to shift classification toward the No Cancer class. Among benign cases, a significant reduction in RBC count emerged as the strongest determinant driving a shift in classification. Overall, CEM effectively explained both the primary features influencing the model's classification results and the magnitude of changes necessary to alter its outputs. Conclusions: Using CEM with ML allowed clear and trustworthy detection of early ovarian cancer. This combined approach shows the promise of XAI in assisting clinicians in making decisions in gynecologic oncology.
dc.identifier.doi10.3390/jcm14176201
dc.identifier.issn2077-0383
dc.identifier.issue17
dc.identifier.orcid0000-0002-6864-7711
dc.identifier.orcid0000-0001-7956-9272
dc.identifier.orcid0000-0002-3805-9214
dc.identifier.pmid40943960
dc.identifier.scopus2-s2.0-105016170947
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/jcm14176201
dc.identifier.urihttps://hdl.handle.net/11616/108556
dc.identifier.volume14
dc.identifier.wosWOS:001569821200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofJournal of Clinical Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectovarian cancer
dc.subjectmachine learning
dc.subjectdeep learning
dc.subjectexplainable artificial intelligence
dc.subjectcontrastive explanation method
dc.subjectdetection
dc.subjectbiomarkers
dc.subjectdiagnostic model
dc.titleExplainable Machine Learning for the Early Clinical Detection of Ovarian Cancer Using Contrastive Explanations
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu