Predicting Inhibitor Development in Hemophilia ‘A’ using Machine Learning: A Comprehensive Approach to Data Preprocessing, Balancing, and Biomarker Identification Using AI on the CHAMP Dataset

This study presents a breakthrough in the early prediction of inhibitor development in Hemophilia 'A' patients, paving the way for personalized and effective treatment programs. The integration of the preprocessing pipeline, Random Forest model, and SHAP analysis offers a novel solution for guiding treatment strategies for HA patients, which could significantly enhance the development of targeted and effective therapies.