Silva, Upeka DeMadanian, SamanehNarayanan, AjitTempleton, John MichaelPoellabauer, ChristianSchneider, Sandra LRubaiat, Rahmina2025-08-122025-08-122025-08-07Studies in Health Technology and Informatics ISSN: 0926-9630 (Print); 0926-9630 (Online), IOS Press, Volume 329: MEDINFO 2025 — Healthcare Smart × Medicine Deep. 1008-1012. doi: 10.3233/SHTI2509910926-96300926-9630http://hdl.handle.net/10292/19665Speech signal analysis to support objective clinical decision-making has gained immense interest, especially in neurological disorders. This research assessed the feasibility of speech analysis on the detection of concussions. Using a speech dataset from 82 concussed and 82 healthy participants, we extracted two speech feature sets focusing on Mel Frequency Cepstral Coefficients (MFCCs) to characterize speech articulation. A machine learning pipeline was developed to discriminate concussion speech from healthy speech by applying Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree (DT) classifiers. All three classifiers trained on the MFCC-based feature set achieved Matthew's correlation coefficient score above 0.5 on the holdout data set. DT model achieved a 78% sensitivity and 75% specificity. The findings of this research serve as proof-of-concept for speech analysis of concussion detection.© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).Concussion DetectionMachine LearningSpeech Analysis0807 Library and Information Studies1117 Public Health and Health ServicesMedical Informatics4203 Health services and systems4601 Applied computingAdultBrain ConcussionFemaleHumansMachine LearningMaleProof of Concept StudySensitivity and SpecificitySupport Vector MachineJournal ArticleOpenAccess10.3233/SHTI250991