Machine Learning Approaches for Enhanced Diagnosis of Hematological Disorders

This research examined the feasibility of utilizing ML algorithms to improve the initial detection and classification of anemia and other blood disorders. Multiple machine learning models (LightGBM, CatBoost, Decision Tree, Gradient Boosting, Random Forest, XGBoost) were evaluated on hematological features, with LightGBM achieving the highest accuracy.