Machine Learning Classification of Cryopathy Syndromes: A Comprehensive Comparative Study

The study presents a comparative analysis of machine learning techniques for the classification of cryopathy syndromes using laboratory data from 2,686 patients across 14 diagnostic categories. Twelve modeling strategies were evaluated, with a soft-voting ensemble of Random Forest and Gradient Boosted Trees achieving the best multiclass performance, while tree-based methods outperformed neural networks. This work highlights the importance of feature engineering in improving classification accuracy and provides a potential framework for clinical decision support in a challenging diagnostic landscape characterized by class imbalance and overlapping symptoms.