Activity Selection to Distinguish Healthy People from Parkinson's Disease Patients Using I-DA

With the aggravation of the population aging problem, Parkinson's disease (PD) and other neurodegenerative diseases of the elderly are not only a medical problem but also an important social problem. Therefore, early detection of PD is particularly important for reducing complications. Currently, the diagnosis of PD is assessed by specialized physicians through the Uniform PD Rating Scale (UPDRS). This limits the detection rate of PD and the timely assessment of disease progression to a certain extent. Moreover, with the development of artificial intelligence, machine learning has been widely and effectively applied to the assessment and monitoring of PD. Therefore, we use machine learning to distinguish between healthy people and PD patients based on UPDRS. In this paper, we collaborated with the First People's Hospital of Yunnan Province to collect exercise data from 15 healthy individuals and 15 PD patients using wearable motion sensors. The analysis found that not all activities collected according to the UPDRS were useful. According to our proposed Indicators for distinguishing activities (I-DA) method as defined in this article, the most differentiated activities are found. Retain the activities that contain the most discriminative information, and use these activities to distinguish between healthy people and PD patients. We verify the effectiveness of this method through experiments. We use k-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM) to execute the classification method. When the selected activities were taken as the whole data set rather than all activities according to our proposed Indicators for distinguishing activities (I-DA) method, the classification accuracy of KNN and XGB were improved by 5.10% and 2.4% respectively. The classification accuracy of SVM was improved by 12.07%. The experimental results show that the accuracy is significantly improved.