TY - JOUR
T1 - Analysis of factors associated with early stage Parkinson's disease based on daily activities and sleeping behaviour disorder
AU - Nastitie, F.
AU - Abdulllah, S.
AU - Nurrohmah, S.
N1 - Funding Information:
This research was funded by Directorate of Research and Development of Universitas Indonesia (DRPM UI) as a grant of Publikasi Terindeks Internasional (PUTI) Prosiding 2020 No. NKB-977/UN2.RST/HKP.05.00/2020. Authors wishing to acknowledge assistance or encouragement from colleagues, special work by technical staff, and financial support from the Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Indonesia. Authors also wishing to acknowledge PPMI, a landmark observational clinical study of PD which is funded by Michael J. Fox Foundation for Parkinson's Research for providing access for the data.
Publisher Copyright:
© 2021 Institute of Physics Publishing. All rights reserved.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/7
Y1 - 2021/1/7
N2 - Parkinson's Disease (PD) is a disorder in human movement coordination system that is characterized by motoric and non-motoric symptoms. At the late stage of PD, clinical diagnosis is relatively easy to detect because the symptoms are clear-cut. However, when the symptoms are often incomplete or subtle, in the initial stage, diagnosis becomes difficult and sometimes the subject still remains undiagnosed or even misdiagnosed. This study was aimed at identifying risk factors in early stage PD based on patient daily activities and rapid eye movement behaviour disorder. Daily activities were measured using the Modified Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part I and part II and sleep behaviour disorder was measured using Rapid eye movement sleeping Behaviour Disorder Screening Questionnaire (RBDSQ). Data analysis was conducted using classification trees with CART algorithm, to classify patients into early stage PD patients or healthy control patients. Missing values were handled using k-Nearest Neighbour (kNN) method. The results were satisfactory, with the classification accuracy of 86.5%, sensitivity 80%, specificity 91.57% and AUC 0.858. It is also found that tremor, dressing difficulty, speech difficulty, RBD questionnaire score, and age are important in differentiating early stage PD from healthy control.
AB - Parkinson's Disease (PD) is a disorder in human movement coordination system that is characterized by motoric and non-motoric symptoms. At the late stage of PD, clinical diagnosis is relatively easy to detect because the symptoms are clear-cut. However, when the symptoms are often incomplete or subtle, in the initial stage, diagnosis becomes difficult and sometimes the subject still remains undiagnosed or even misdiagnosed. This study was aimed at identifying risk factors in early stage PD based on patient daily activities and rapid eye movement behaviour disorder. Daily activities were measured using the Modified Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part I and part II and sleep behaviour disorder was measured using Rapid eye movement sleeping Behaviour Disorder Screening Questionnaire (RBDSQ). Data analysis was conducted using classification trees with CART algorithm, to classify patients into early stage PD patients or healthy control patients. Missing values were handled using k-Nearest Neighbour (kNN) method. The results were satisfactory, with the classification accuracy of 86.5%, sensitivity 80%, specificity 91.57% and AUC 0.858. It is also found that tremor, dressing difficulty, speech difficulty, RBD questionnaire score, and age are important in differentiating early stage PD from healthy control.
UR - http://www.scopus.com/inward/record.url?scp=85100818633&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1722/1/012045
DO - 10.1088/1742-6596/1722/1/012045
M3 - Conference article
AN - SCOPUS:85100818633
SN - 1742-6588
VL - 1722
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012045
T2 - 10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020
Y2 - 12 October 2020 through 15 October 2020
ER -