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This study presents a comprehensive approach to diagnosing melasma using ensemble methods. Real-world clinical data were collected from Quy Hoa Central Leprosy-Dermatology Hospital in Vietnam, integrating both community-based insights and expert dermatological assessments. We employed state-of-the-art ensemble learning algorithms—XGBoost, CatBoost, and LightGBM—to identify key contributing factors. These gradient boosting algrithms leverage decision trees as their foundational structure, a technique recognized for its efficacy in supervised learning contexts, particularly within classification and regression tasks. Furthermore, SHAP (SHapley Additive exPlanations) was used to interpret model outputs, providing transparent insights into how each feature influences predictions. Through this approach, we aim to not only improve diagnostic accuracy but also enhance trust in AI-assisted decisions by making the model’s reasoning more transparent. Ultimately, our objective is to develop a clinically applicable, interpretable, and data-driven tool for early detection and management of melasma. Such a tool can support dermatologists in delivering more precise, personalized treatments and contribute to better long-term outcomes for patients.

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Ensemble Methods for Melasma Diagnosis and Causal Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Van Lam Ho,
  • Le Thi Thu Ngan,
  • Tran Xuan Viet

摘要

This study presents a comprehensive approach to diagnosing melasma using ensemble methods. Real-world clinical data were collected from Quy Hoa Central Leprosy-Dermatology Hospital in Vietnam, integrating both community-based insights and expert dermatological assessments. We employed state-of-the-art ensemble learning algorithms—XGBoost, CatBoost, and LightGBM—to identify key contributing factors. These gradient boosting algrithms leverage decision trees as their foundational structure, a technique recognized for its efficacy in supervised learning contexts, particularly within classification and regression tasks. Furthermore, SHAP (SHapley Additive exPlanations) was used to interpret model outputs, providing transparent insights into how each feature influences predictions. Through this approach, we aim to not only improve diagnostic accuracy but also enhance trust in AI-assisted decisions by making the model’s reasoning more transparent. Ultimately, our objective is to develop a clinically applicable, interpretable, and data-driven tool for early detection and management of melasma. Such a tool can support dermatologists in delivering more precise, personalized treatments and contribute to better long-term outcomes for patients.