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Open AccessArticle

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

1
Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100225, Taiwan
2
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan
3
Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan
4
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
5
Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei 100233, Taiwan
6
Graduate Institue of Psychology, National Taiwan University, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(18), 6914; https://doi.org/10.3390/ijms21186914
Received: 31 August 2020 / Revised: 17 September 2020 / Accepted: 18 September 2020 / Published: 21 September 2020
(This article belongs to the Special Issue Peripheral Biomarkers in Neurodegenerative Diseases 2.0)
Easily accessible biomarkers for Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders. View Full-Text
Keywords: Alzheimer’s disease; Parkinson’s disease; frontotemporal dementia; neurodegenerative disorders; biomarkers; deep learning model; linear discriminant analysis; classification; multivariate imputation by chained equations Alzheimer’s disease; Parkinson’s disease; frontotemporal dementia; neurodegenerative disorders; biomarkers; deep learning model; linear discriminant analysis; classification; multivariate imputation by chained equations
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Lin, C.-H.; Chiu, S.-I.; Chen, T.-F.; Jang, J.-S.R.; Chiu, M.-J. Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model. Int. J. Mol. Sci. 2020, 21, 6914.

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