Towards Early Prediction of Amyotrophic Lateral Sclerosis Empowered by Machine Learning and Clinical Big Data

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by substantial symptom heterogeneity and overlap with other neurological conditions, often delaying diagnosis. This study developed a consensus-based feature selection framework to identify a stable and parsimonious minimal feature set for early ALS prediction using large-scale observational data. Using multi-year medical claims and multi-site EHRs, we identified 1,716 ALS cases with matched controls. The approach integrated variability across sample, task, and model dimensions to isolate features predictive up to 18 months before diagnosis. Predictive models using LASSO regression and GBT were evaluated with AUROC and classification metrics. The resulting nine-feature set achieved AUROC values above 0.85 across time windows. The GBT model was further evaluated in musculoskeletal, nervous system, and limb or bulbar subgroups, demonstrating reliable discrimination and preserved sensitivity and specificity. These findings highlight the potential of stable minimal feature sets to support earlier ALS identification.