A Transparent AI-Driven Multiclass Decision Support System for Thyroid Risk Prediction Using Machine Learning and Deep Learning Approaches

• Autorzy: Ouartani Siouar , Taleb Nora

Identyfikatory

DOI

10.2478/fcds-2025-0019

• Języki publikacji: EN

Abstrakty

EN

Early and accurate diagnosis of thyroid disorders is essential due to their prevalence and health impact. To enhance interpretability in clinical settings, we propose a comprehensive workflow for transparent thyroid disease prediction using a multiclass classification problem with five diagnostic categories. A dataset of 9172 samples with 31 features was used to train various machine and deep learning models. A dual-layered framework combining Feature Selection (ETC, MI, RFE) and Explainable AI (SHAP, LIME) improved performance and transparency. Gradient Boosting achieved the highest accuracy (0.97). SHAP explained global feature influence, while LIME clarified individual predictions. Our approach supports interpretable, reliable AI-based diagnostic tools for thyroid disorder classification.

Słowa kluczowe

EN

thyroid disease diagnosis; Medical Decision Support System; MDSS; machine learning; deep learning; explainable AI; XAI; feature selection; model interpretability; LIME; SHAP

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2025
• Tom: Vol. 50, No. 4
• Strony: 473--509
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Ouartani Siouar

• Complex Systems Engineering Laboratory (LISCO), Computer Science Department, Badji Mokhtar University, Annaba, Algeria

autor

Taleb Nora

• Complex Systems Engineering Laboratory (LISCO), Computer Science Department, Badji Mokhtar University, Annaba, Algeria

Bibliografia

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