Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches

• Published in: Annals of biomedical engineering Vol. 46; no. 12; pp. 2057 - 2068
• Main Authors: Rovini, Erika, Maremmani, Carlo, Moschetti, Alessandra, Esposito, Dario, Cavallo, Filippo
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2018; Springer Nature B.V
• Subjects: Adults; Aged; Artificial intelligence; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Classification; Feature extraction; Female; Gait - physiology; Humans; Inertial sensing devices; Learning algorithms; Machine learning; Male; Middle Aged; Motors; Movement disorders; Neurodegenerative diseases; Olfaction disorders; Olfaction Disorders - diagnosis; Olfaction Disorders - physiopathology; Parameter identification; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson's disease; Patients; Quality of life; Supervised Machine Learning; Decision support systems; Idiopathic hyposmia; Supervised learning; Motion analysis; Inertial wearable sensors

Millions of people worldwide are affected by Parkinson’s disease (PD), which significantly worsens their quality of life. Currently, the diagnosis is based on assessment of motor symptoms, but interest toward non-motor symptoms is increasing, as well. Among them, idiopathic hyposmia (IH) is associat...