A machine learning model for prediction of sarcopenia in patients with Parkinson's Disease

• Published in: PloS one 2024-01, Vol.19 (1), p.e0296282-e0296282
• Main Authors: Kim, Minkyeong, Kim, Doeon, Kang, Heeyoung, Park, Seongjin, Kim, Shinjune, Yoo, Jun-Il
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Ankle; Biology and Life Sciences; Business metrics; Cameras; Care and treatment; Complications and side effects; Diagnosis; Disease; Gait; Gait recognition; Hip; Humans; Joints (anatomy); Learning algorithms; Machine Learning; Medical research; Medicine and Health Sciences; Medicine, Experimental; Movement disorders; Musculoskeletal system; Neurodegenerative diseases; Parameters; Parkinson Disease - complications; Parkinson's disease; Patients; Prognosis; Risk factors; Sarcopenia; Sarcopenia - complications; Sarcopenia - diagnosis; Smartphones; Variables; Velocity; Walking
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0296282

Patients with Parkinson's disease (PD) have an increased risk of sarcopenia which is expected to negatively affect gait, leading to poor clinical outcomes including falls. In this study, we investigated the gait patterns of patients with PD with and without sarcopenia (sarcopenia and non-sarcopenia groups, respectively) using an app-derived program and explored if gait parameters could be utilized to predict sarcopenia based on machine learning. Clinical and sarcopenia profiles were collected from patients with PD at Hoehn and Yahr (HY) stage ≤ 2. Sarcopenia was defined based on the updated criteria of the Asian Working Group for Sarcopenia. The gait patterns of the patients with and without sarcopenia were recorded and analyzed using a smartphone application. The random forest model was applied to predict sarcopenia in patients with PD. Data from 38 patients with PD were obtained, among which 9 (23.7%) were with sarcopenia. Clinical parameters were comparable between the sarcopenia and non-sarcopenia groups. Among various clinical and gait parameters, the average range of motion of the hip joint showed the highest association with sarcopenia. Based on the random forest algorithm, the combined difference in knee and ankle angles from standing still before walking to the maximum angle during walking (Kneeankle_diff), the difference between the angle when standing still before walking and the maximum angle during walking for the ankle (Ankle_dif), and the min angle of the hip joint (Hip_min) were the top three features that best predict sarcopenia. The accuracy of this model was 0.949. Using smartphone app and machine learning technique, our study revealed gait parameters that are associated with sarcopenia and that help predict sarcopenia in PD. Our study showed potential application of advanced technology in clinical research.