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Explainable AI framework for improved Thalassemia mental health classification and feature selection.

Shahriar Siddique Ayon1, Abdullah Al Mamun2, Md Ebrahim Hossain1

  • 1Department of Computer Science and Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh.

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This study introduces a new AI framework, AMSE-DFI, to identify mental health predictors in Thalassemia patients. It improves early detection and personalized care by analyzing complex patient data.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Psychosomatic Medicine

Background:

  • Mental health issues in Thalassemia patients are frequently underestimated, impacting their quality of life.
  • Conventional statistical and machine learning methods struggle with the complex, nonlinear interactions between psychosocial and clinical factors in Thalassemia.
  • Accurate prediction and interpretation of mental health status in Thalassemia patients remain challenging.

Purpose of the Study:

  • To develop and validate a novel feature selection framework, Adaptive Multi-Stage Ensemble with Dynamic Feature Interaction (AMSE-DFI), for identifying mental health predictors in Thalassemia.
  • To enhance the accuracy and interpretability of mental health assessments in Thalassemia patients.
  • To provide a practical tool for early detection and personalized management of mental health challenges in Thalassemia care.

Main Methods:

  • Developed AMSE-DFI, integrating mutual information, ensemble learning, and graph attention mechanisms for dynamic feature interaction.
  • Utilized SF-36 health survey data from 356 Bangladeshi Thalassemia patients.
  • Employed Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance and Local Interpretable Model-Agnostic Explanations (LIME) for model interpretability.

Main Results:

  • AMSE-DFI identified key predictors of mental health in Thalassemia patients, including total SF score, role emotional, and physical health summary.
  • The proposed framework demonstrated superior predictive reliability and generalization compared to conventional methods.
  • LIME provided clear, interpretable insights into feature impact on individual patient outcomes.

Conclusions:

  • AMSE-DFI offers a robust and interpretable approach for identifying mental health challenges in Thalassemia patients.
  • The framework facilitates a deeper understanding of the interplay between clinical and psychosocial factors.
  • This AI-driven tool supports clinicians in the early detection and personalized management of mental health in Thalassemia care.