Algorithmic Comparison of Gradient Boosting and Traditional Machine Learning Models for Early Detection and Clinical Risk Assessment of Hemochromatosis

Early detection of hemochromatosis, a hereditary disorder causing too high iron accumulation, helps to avoid major problems. With an eye towards accuracy and risk stratification, this paper assesses machine learning approaches for early hemochromatosis (HC) detection. They ran logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision trees. With an accuracy of 90.1 %, XGB outperformed traditional models; following performances were RF at 88.7%, LR at 86.4%, and DT at 85.2%. The XGB ensemble approach controls links and complex patterns. Stratifying risk, the features of the XGB and RF models expose illness biomarkers. Early hemochromatosis identification is a perfect paradigm for XGB; we found that clinical decision support systems in the management of hereditary diseases benefit much from increased accuracy and interpretability.