Work

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

Year: 2026

Type: article

Abstract: 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 Mel-Frequency Cepstral Coefficients—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 internal robustness was supported by consistent performance across stratified cross-validation folds and three recording sites. In conclusion, PWEL is practical, scalable, and making it a promising tool for detecting neurodegenerative disorders.

Source: Scientific Reports

DOI: 10.1038/s41598-026-73857-1