Muñoz-Ospina Beatriz, Alvarez-Garcia Daniela, Clavijo-Moran Hugo Juan Camilo, Valderrama-Chaparro Jaime Andrés, García-Peña Melisa, Herrán Carlos Alfonso, Urcuqui Christian Camilo, Navarro-Cadavid Andrés, Orozco Jorge
Fundación Valle del Lili, Departamento de Neurología, Cali, Colombia.
Fundación Valle del Lili, Departamento de Neurocirugía, Cali, Colombia.
Front Hum Neurosci. 2022 May 19;16:826376. doi: 10.3389/fnhum.2022.826376. eCollection 2022.
The assessments of the motor symptoms in Parkinson's disease (PD) are usually limited to clinical rating scales (MDS UPDRS III), and it depends on the clinician's experience. This study aims to propose a machine learning technique algorithm using the variables from upper and lower limbs, to classify people with PD from healthy people, using data from a portable low-cost device (RGB-D camera). And can be used to support the diagnosis and follow-up of patients in developing countries and remote areas.
We used KinecteMotion system to capture the spatiotemporal gait data from 30 patients with PD and 30 healthy age-matched controls in three walking trials. First, a correlation matrix was made using the variables of upper and lower limbs. After this, we applied a backward feature selection model using R and Python to determine the most relevant variables. Three further analyses were done using variables selected from backward feature selection model (Dataset A), movement disorders specialist (Dataset B), and all the variables from the dataset (Dataset C). We ran seven machine learning models for each model. Dataset was divided 80% for algorithm training and 20% for evaluation. Finally, a causal inference model (CIM) using the DoWhy library was performed on Dataset B due to its accuracy and simplicity.
The Random Forest model is the most accurate for all three variable Datasets (Dataset A: 81.8%; Dataset B: 83.6%; Dataset C: 84.5%) followed by the support vector machine. The CIM shows a relation between leg variables and the arms swing asymmetry (ASA) and a proportional relationship between ASA and the diagnosis of PD with a robust estimator (1,537).
Machine learning techniques based on objective measures using portable low-cost devices (KinecteMotion) are useful and accurate to classify patients with Parkinson's disease. This method can be used to evaluate patients remotely and help clinicians make decisions regarding follow-up and treatment.
帕金森病（PD）运动症状的评估通常局限于临床评分量表（MDS UPDRS III），且依赖于临床医生的经验。本研究旨在提出一种机器学习技术算法，利用来自上肢和下肢的变量，通过便携式低成本设备（RGB-D相机）的数据，将帕金森病患者与健康人进行分类。并且可用于支持发展中国家和偏远地区患者的诊断和随访。
我们使用KinecteMotion系统在三次步行试验中，从30例帕金森病患者和30名年龄匹配的健康对照者中捕捉时空步态数据。首先，使用上肢和下肢的变量制作相关矩阵。在此之后，我们应用R和Python的向后特征选择模型来确定最相关的变量。使用从向后特征选择模型中选择的变量（数据集A）、运动障碍专家（数据集B）以及数据集中的所有变量（数据集C）进行了另外三项分析。每个模型我们运行了七种机器学习模型。数据集80%用于算法训练，20%用于评估。最后，由于其准确性和简单性，对数据集B使用DoWhy库进行了因果推断模型（CIM）。
随机森林模型对所有三个变量数据集（数据集A：81.8%；数据集B：83.6%；数据集C：84.5%）最为准确，其次是支持向量机。因果推断模型显示腿部变量与手臂摆动不对称（ASA）之间存在关系，并且ASA与帕金森病诊断之间存在比例关系，具有稳健估计值（1537）。
基于使用便携式低成本设备（KinecteMotion）的客观测量的机器学习技术，对于帕金森病患者的分类是有用且准确的。这种方法可用于远程评估患者，并帮助临床医生做出关于随访和治疗的决策。
