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Okan Bursa

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Conference Jul 2026

Explainable AI-Based Credit Default Risk Modeling on Multi-Table Data

This study addresses the prediction of credit default risk using the Home Credit Default Risk dataset. Due to the approximately eight percent imbalanced class distribution in the data structure, an intensive feature engineering pipeline that reduces multiple tables to the customer level was applied before modeling. The implementation was designed to include behavioral summaries generated from external credit history, previous applications, installment behavior, credit card transactions, and POS cash records. Missing value handling, encoding, and multicollinearity reduction steps were performed on the obtained features. LightGBM was selected as the classification model, and the hyperparameters were optimized with Optuna under cross validation. In the best configuration, the mean AUC value was observed as 0.78682 and the mean PR AUC value as 0.28015. In order for the probabilities to be used more reliably in decision making processes, Platt scaling and SHAP based explainability analysis were applied.

Büşra Kurun, Okan Bursa · 0 citations
Conference Jul 2026

PharyTriFuse: Knowledge- and LLM-Augmented Deep Learning for Bacterial Pharyngitis Detection from Smartphone Throat Images

Bacterial pharyngitis requires timely antibiotic treatment, whereas most non-bacterial cases are self-limited; diagnostic errors may therefore lead to missed infections or unnecessary antibiotic use. This study proposes PharyTriFuse, a multimodal framework that integrates throat-image analysis with large language model (LLM) reasoning and a medical knowledge graph (KG) to classify bacterial versus non-bacterial pharyngitis from smartphone-acquired oropharyngeal images. Experiments were conducted on the public PGUPharyngitis dataset over 742 images using a stratified 72%/8%/20% train/validation/test split. Images were standardized using CLAHE and redness enhancement to reduce acquisition variability. Two visual backbones (EfficientNet-B4 and ConvNeXt-Base) were evaluated under four configurations: AI-only, AI+LLM, AI+KG, and AI+LLM+KG. Performance was assessed using standard classification metrics and inference efficiency. Results show that incorporating LLM reasoning and structured medical knowledge improves classification performance over vision-only baselines while maintaining real-time inference capability under certain configurations. These findings suggest that multimodal AI systems can enhance smartphone-based decision support for pharyngitis assessment.

Ö. Koca, Murat Uçar, Okan Bursa · 0 citations