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

## TL;DR

This paper introduces a new AI framework to better understand and predict mental health in Thalassemia patients by identifying key factors and making the model's decisions explainable.

## Contribution

The novel AMSE-DFI framework combines mutual information, ensemble learning, and graph attention for dynamic feature selection and improved interpretability.

## Key findings

- AMSE-DFI outperformed traditional methods in predicting mental health outcomes for Thalassemia patients.
- Key predictors like total SF score and physical health summary were identified as important for mental health.
- LIME-based explanations provided actionable insights for clinicians, enhancing model transparency.

## Abstract

Mental health challenges in Thalassemia patients are often overlooked, despite their significant impact on quality of life. Traditional statistical and machine learning approaches often fail to capture the complex, nonlinear relationships between psychosocial and clinical variables, limiting both accuracy and interpretability. To overcome this, we propose the Adaptive Multi-Stage Ensemble with Dynamic Feature Interaction (AMSE-DFI)—a novel feature selection framework that dynamically integrates mutual information, ensemble learning, and graph attention mechanisms to capture intricate feature interdependencies often missed by traditional approaches. Using the SF-36 health survey data from 356 Bangladeshi patients, AMSE-DFI effectively identified key predictors such as total SF score, role emotional, and physical health summary, which collectively reflect both physical and psychological well-being. The model outperformed conventional approaches, showing strong predictive reliability and robust generalization, with SMOTE effectively addressing class imbalance in the clinical data. Importantly, Local Interpretable Model-Agnostic Explanations (LIME) based explainable AI offered clear, interpretable insights into how key features affect individual patient outcomes, making the model more understandable and actionable for clinicians. This framework provides a practical, transparent tool to support early detection and personalized management of mental health challenges in Thalassemia care.