Department of Electrical Engineering, Indian Institute of Technology Delhi, India.
Department of Electrical Engineering, Indian Institute of Technology Delhi, India.
Int J Med Inform. 2018 Nov;119:75-87. doi: 10.1016/j.ijmedinf.2018.09.008. Epub 2018 Sep 9.
Early detection of Parkinson's disease (PD) is important which can enable early initiation of therapeutic interventions and management strategies. However, methods for early detection still remain an unmet clinical need in PD. In this study, we use the Patient Questionnaire (PQ) portion from the widely used Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to develop prediction models that can classify early PD from healthy normal using machine learning techniques that are becoming popular in biomedicine: logistic regression, random forests, boosted trees and support vector machine (SVM). We carried out both subject-wise and record-wise validation for evaluating the machine learning techniques. We observe that these techniques perform with high accuracy and high area under the ROC curve (both >95%) in classifying early PD from healthy normal. The logistic model demonstrated statistically significant fit to the data indicating its usefulness as a predictive model. It is inferred that these prediction models have the potential to aid clinicians in the diagnostic process by joining the items of a questionnaire through machine learning.
帕金森病 (PD) 的早期检测很重要，这可以使治疗干预和管理策略尽早开始。然而，PD 的早期检测方法仍然是未满足的临床需求。在这项研究中，我们使用广泛使用的运动障碍协会-统一帕金森病评定量表 (MDS-UPDRS) 的患者问卷 (PQ) 部分，使用在生物医学中越来越流行的机器学习技术来开发可以使用机器学习技术从健康正常人群中分类早期 PD 的预测模型：逻辑回归、随机森林、提升树和支持向量机 (SVM)。我们进行了基于受试者和基于记录的验证，以评估机器学习技术。我们观察到，这些技术在对早期 PD 与健康正常人群进行分类时具有高精度和高 ROC 曲线下面积（均>95%）。逻辑模型对数据具有统计学意义上的拟合，表明其作为预测模型的有用性。可以推断，这些预测模型通过机器学习将问卷的项目结合起来，有可能帮助临床医生进行诊断过程。
