Distinguishing amyotrophic lateral sclerosis from radiculopathy using machine learning to analyze nerve conduction data.

ABSTRACT: Amyotrophic lateral sclerosis (ALS) is a rare, fatal, and irreversible disease that shares some key clinical features with radiculopathy, including muscle atrophy, muscle cramps, and fasciculation. The aim of this study was to find a reliable method to differentiate these two diseases. Machine learning was used to discover new clinical biomarkers for the differential diagnosis of ALS from radiculopathy using nerve conduction study (NCS) data from patients. Data preparation and feature selection were performed by a random forest classifier algorithm, as well as a confusion matrix tool for model selection. After selecting the minimum number of features and the best algorithm, grid search cross-validation was used to optimize the hyperparameters of the chosen algorithm. 77 features were ranke

SUBMITTER: Ariaei A 

PROVIDER: S-EPMC12644858 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature