Machine Learning Approaches in the Detection of Amyotrophic Lateral Sclerosis Disease Using Orofacial Gestures

Mara Hajdu-Macelaru, Rares Chiuzbaian, Petrica C. Pop

Abstract: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects nerve cells in the brain and spinal cord, specifically the motor neurons. As far as we know, there is no single test that can definitively diagnose ALS, and the diagnosis is often based on a integration of clinical findings, medical history, physical examination and various tests to rule out other possible conditions and confirm the diagnosis. The present work proposes four machine learning (ML) algorithms: K-Nearest Neighbors, the Iterative Di-chotomizer 3, Naive Bayes and Logistic Regression to help the diagnosis of early signs of ALS disease. In order to test the proposed ML algorithms, we used the only existing data set, created by the Sunnybrook Research Institute in Toronto. Using the extracted images from the videos of the participants, we developed a system of recognition based on orofacial gestures of the early signs of ALS. The achieved experimental results show that the described ML

External IDs:dblp:conf/icaart/Hajdu-MacelaruC25