An Explainable AI-Based Decision Support System for Teaching and Classifying Hair Loss Types
DOI:
https://doi.org/10.46328/ijtes.8226Keywords:
Decision support system, Explainable Artificial Intelligence, Hair loss classification, Leakage-resistant pipeline, Multi-class classificationAbstract
Hair loss is a multifactorial condition that requires accurate classification to support reliable and personalized decision-making. However, many existing machine learning approaches suffer from data leakage and limited interpretability, reducing their robustness and practical applicability in decision support systems. This study proposes a leakage-resistant machine learning framework for multi-class hair loss classification, integrating explainable artificial intelligence to enhance transparency and reliability. The framework employs a unified preprocessing pipeline within nested cross-validation to prevent information leakage, while SMOTEENN is used to address class imbalance. Several algorithms, including Decision Tree, Random Forest, K-Nearest Neighbors, Logistic Regression, and Extreme Gradient Boosting, are evaluated within this pipeline. Experimental results indicate that Extreme Gradient Boosting achieves the best performance, with an accuracy of 0.8300, F1-score of 0.7908, and AUC of 0.9305 in nested cross-validation. Evaluation on a holdout dataset demonstrates stable generalization, achieving an accuracy of 0.8250, F1-score of 0.7517, and AUC of 0.9182. Furthermore, the integration of explainable artificial intelligence enables interpretable predictions that can be utilized in a rule-based decision support system. Overall, the proposed framework provides a robust, transparent, and leakage-resistant solution for reliable machine learning-based classification in practical applications.
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