Development of an Explainable AI Framework for Early Prediction of Animal Hereditary Diseases Using Genomic Biomarkers
2026 International Conference on Circuit, Systems and Communication, ICCSC 2026, Hybrid, Fez, Fas, 2 - 03 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/iccsc71566.2026.11650564
- Basıldığı Şehir: Hybrid, Fez
- Basıldığı Ülke: Fas
- Anahtar Kelimeler: clinical decision support, Explainable AI (XAI), hereditary disorders, nuclear morphology, SHAP values, XGBoost
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
Hereditary cancer syndromes (i.e. BRCA1/2-breast cancer) represent a small portion of around 6% of the population worldwide with genetic diseases, but their histopathological diagnosis is still difficult. In this paper, we present a clinical decision support system that combines an improved XGBoost classifier with SHapley Additive Explanations (SHAP) to provide high predictive accuracy and interpretable AI (XAI) in breast cancer diagnosis based on nuclear morphological features. The proposed approach employs a five-stage framework, namely: (1) Data collection from the Wisconsin Breast Cancer Diagnostic (WDBC) dataset, (2) missing value treatment by using multiple imputation by chained equations (MICE), (3) feature normalization, (4) biomarker selection using recursive feature elimination with cross validation (RFECV), and (5) class imbalance addressing by synthetic minority over-sampling technique (SMOTE). Hyperparameter tuning was done with Optuna using the Tree-structured Parzen Estimator (TPE) for 300 trials. The XGBoost model, evaluated on a test data consisting of 114 samples (20% of the total 569 samples), attained the highest accuracy of 96% and recall of 97% for class malignant, with a close to perfection ROC AUC of 0.99. SHAP results demonstrated that morphological characteristics (convex points and concavity, in particular) were the most important features, which is in line with histopathological understanding of nuclear alterations in hereditary cancer syndromes. Local waterfall plots ensured case-specific interpretability by breaking down single predictions into additive feature contributions. Although the WDBC dataset does not contain direct labels of genotype, the designed framework acts as a reproducible proof-of-concept for transparent, high-performance diagnosis of breast cancer with potential for application to hereditary syndromes, and veterinary histopathology incorporating appropriate species specific validation.