Enhancing Alpha Thalassemia Screening: A Comparative Study of Multiple Machine Learning Classifiers and Interpretation using Explainable AI

This study introduces an innovative paradigm for Alpha thalassemia detection by integrating machine learning, hyperparameter tuning, and explainable artificial intelligence (XAI) techniques. Through a comparative analysis, boosting algorithms, especially XG Boost, demonstrate remarkable efficacy in accurately identifying Alpha thalassemia conditions. The iterative hyperparameter tuning process enhances the final XG Boost model’s accuracy to an impressive $90.11 \%$. The incorporation of XAI methods, such as SHAP values and LIME, provides a nuanced understanding of feature importance and individual predictions, elevating model interpretability. This research’s significance lies in its potential to transform Alpha thalassemia screening, offering precision, resilience, and transparency. The amalgamation of machine learning classifiers, hyperparameter tuning, and XAI techniques contributes to advanced screening tools, fostering trust among clinicians and researchers.