The neuropsychology assessment for identifying dementia in parkinson’s disease patients using a deep neural network

Nur Hafieza, Ismail and Nur Shazwani, Kamarudin and Ahmad Fakhri, Ab. Nasir (2021) The neuropsychology assessment for identifying dementia in parkinson’s disease patients using a deep neural network. In: 7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021, 24-26 Aug. 2021 , Pekan, Malaysia. pp. 238-243.. ISBN 978-166541407-4

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Abstract

Parkinson’s Disease (PD) patients have a high risk of developing dementia at least a year after the diagnosis. PD-Dementia affects both the physical and mental function that can gradually worsen the condition of the patients over time. This work proposed a framework for detecting dementia among PD patients based on neuropsychological assessment. This work classifies samples using the Montreal Cognitive Assessment (MoCA) scores as a guideline. It is classified into three categories, which are No Dementia, PD-MCI, and PD-Dementia. The work continues with designing a Deep Neural Network (DNN) architecture specific for analyzing electronic health records for PDDementia detection. Then, it compares the proposed model with the other five baseline methods. The experiment results present that the proposed DNN presents the highest result of 97.5%. This result shows that this proposed model is able to identify early dementia in PD patients from non-motor symptoms.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Dementia; Parkinson’s disease; Multivariate data; Montreal cognitive assessment; Machine learning; Deep learning
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computing
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 02 Sep 2022 07:22
Last Modified: 02 Sep 2022 07:22
URI: http://umpir.ump.edu.my/id/eprint/33135
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