Robust and Explainable Stage Prediction in Duchenne Muscular Dystrophy

Abstract

Duchenne muscular dystrophy (DMD) is one of the life-threatening rare genetic disease affecting millions of male minors across the globe. Given its progressive nature, we can demarcate the various stages of DMD through the loss of muscular movements, ambulation, respiratory difficulties, and cardiac dysfunction. In this work, we employ machine learning models for understanding the progression of DMD through the prediction of its stages. Our attempts to predict the stages of DMD on the data collected by Molecular Diagnostics, Counseling, Care and Research Center (MDCRC) from 223 visits of 90 subjects demonstrate more than 80% accuracy with the state-of-the-art methods. We further study the biological/physiological importance of features in characterizing the stages of DMD.