A Machine Learning Approach to Detect Parkinson’s Disease by Looking at Gait Alterations

Cristina Tîrnăucă (), Diana Stan, Johannes Mario Meissner, Diana Salas-Gómez, Mario Fernández-Gorgojo and Jon Infante
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Cristina Tîrnăucă: Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, 39005 Santander, Spain
Diana Stan: Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, 39005 Santander, Spain
Johannes Mario Meissner: Computer Science Department, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
Diana Salas-Gómez: Movement Analysis Laboratory, Physiotherapy School Cantabria, Escuelas Universitarias Gimbernat (EUG), Universidad de Cantabria, 39300 Torrelavega, Spain
Mario Fernández-Gorgojo: Movement Analysis Laboratory, Physiotherapy School Cantabria, Escuelas Universitarias Gimbernat (EUG), Universidad de Cantabria, 39300 Torrelavega, Spain
Jon Infante: Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), 28029 Madrid, Spain

Mathematics, 2022, vol. 10, issue 19, 1-25

Abstract: Parkinson’s disease (PD) is often detected only in later stages, when about 50% of nigrostriatal dopaminergic projections have already been lost. Thus, there is a need for biomarkers to monitor the earliest phases, especially for those that are at higher risk. In this work, we explore the use of machine learning methods to diagnose PD by analyzing gait alterations via an inertial sensors system that participants in the study wear while walking down a 15 m long corridor in three different scenarios. To achieve this goal, we have trained six well-known machine learning models: support vector machines, logistic regression, neural networks, k nearest neighbors, decision trees and random forest. We thoroughly explored several ways to mitigate the problems derived from the small amount of available data. We found that, while achieving accuracy rates of over 70% is quite common, the accuracy of the best model trained is only slightly above the 80% mark. This model has high precision and specificity (over 90%), but lower sensitivity (only 71%). We believe that these results are promising, especially given the size of the population sample (41 PD patients and 36 healthy controls), and that this research venue should be further explored.

Keywords: Parkinson’s disease; gait alterations; classification; support vector machine; logistic regression; neural networks; k nearest neighbors; decision trees; random forest (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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