Towards a Machine Learning Empowered Prognostic Model for Predicting Disease Progression for Amyotrophic Lateral Sclerosis.

Abstract

Amyotrophic lateral sclerosis (ALS) is a rare and devastating neurodegenerative disorder that is highly heterogeneous and invariably fatal. Due to the unpredictable nature of its progression, accurate tools and algorithms are needed to predict disease progression and improve patient care. To address this need, we developed and compared an extensive set of screener-learner machine learning models to accurately predict the ALS Function-Rating-Scale (ALSFRS) score reduction between 3 and 12 months, by paring 5 state-of-arts feature selection algorithms with 17 predictive models and 4 ensemble models using the publicly available Pooled Open Access Clinical Trials Database (PRO-ACT). Our experiment showed promising results with the blender-type ensemble model achieving the best prediction accuracy and highest prognostic potential.