Performance-weighted ensemble learning for detecting patients with FTD and ALS from short reading tasks

We developed a novel machine learning model named performance-weighted ensemble learning (PWEL) to detect frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) using speech from word and sentence reading tasks. Overall, 197 participants (30 with FTD, 74 with ALS, and 93 healthy controls) were enrolled. The brief oral reading tasks consisted of 16 materials adapted from the Western Aphasia Battery and took 1 min to complete. For each task, 405 speech features (i.e., 384 acoustic, 17 linguistic, and 4 temporal) were extracted. Five-fold cross-validation highlighted acoustic features—especially MFCCs—as key discriminators of FTD and ALS from healthy controls. PWEL, which included under-bagging and adaptive task selection, achieved an area under the curve of 0.840, a sensitivity of 0.828, and an overall accuracy of 0.792. The sensitivity for FTD subtypes was 0.700-0.933, and that for ALS reached 0.812. Our proposed model surpassed both a simple ensemble learning model and task-wise classification in overall sensitivity, accuracy, and macro-F1. Its robust generalizability was demonstrated by consistent performance across stratified cross-validation folds and three recording sites. In conclusion, PWEL is practical, scalable, and generalizable across multiple sites, making it a promising tool for detecting neurodegenerative disorders.