Adaptive Machine Learning Feature Selection for Rare Disease Diagnosis

Design an automated feature selection algorithm specifically tailored for rare disease diagnostic modeling. Create a system that can dynamically evaluate feature importance across heterogeneous medical datasets, handling high-dimensionality and sparse data challenges. Implement ensemble feature ranking methods that can work with limited training samples and provide interpretable feature importance scores for clinical researchers.