Preprint Article Version 1 This version is not peer-reviewed

Developing a Machine Learning Model to Predict the Risk of Cognitive Decline in Early Parkinson's Disease

Raziyeh Mohammadi
,
Samuel YE Ng
,
Jayne Y Tan
,
Adeline SL Ng
,
Xiao Deng
,
Xinyi Choi
,
Dede L Heng
,
Shermyn Neo
,
Zheyu Xu
,
Kay-Yaw Tay
,
Wing-Lok Au
,
Eng-King Tan
,
Louis CS Tan
,
Ewout W Steyerberg
,
William Greene
,
Seyed Ehsan Saffari
*
Version 1 : Received: 2 November 2024 / Approved: 4 November 2024 / Online: 5 November 2024 (09:26:12 CET)

How to cite: Mohammadi, R.; Ng, S. Y.; Tan, J. Y.; Ng, A. S.; Deng, X.; Choi, X.; Heng, D. L.; Neo, S.; Xu, Z.; Tay, K.-Y.; Au, W.-L.; Tan, E.-K.; Tan, L. C.; Steyerberg, E. W.; Greene, W.; Saffari, S. E. Developing a Machine Learning Model to Predict the Risk of Cognitive Decline in Early Parkinson's Disease. Preprints 2024, 2024110261. https://doi.org/10.20944/preprints202411.0261.v1 Mohammadi, R.; Ng, S. Y.; Tan, J. Y.; Ng, A. S.; Deng, X.; Choi, X.; Heng, D. L.; Neo, S.; Xu, Z.; Tay, K.-Y.; Au, W.-L.; Tan, E.-K.; Tan, L. C.; Steyerberg, E. W.; Greene, W.; Saffari, S. E. Developing a Machine Learning Model to Predict the Risk of Cognitive Decline in Early Parkinson's Disease. Preprints 2024, 2024110261. https://doi.org/10.20944/preprints202411.0261.v1

Abstract

Background: Cognitive decline (CD) is a significant concern in Parkinson's disease (PD), highlighting the need for reliable risk prediction models for early intervention. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson's Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment (MoCA) over two consecutive years. Four ML methods—AutoScore, Random Forest, K-Nearest Neighbors and Neural Network—were applied using baseline demographics, clinical assessments and blood biomarkers. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusion: ML-based models offer potential for early identification of patients at high risk of CD, facilitating targeted interventions and improving patient outcomes in PD management.

Keywords

Cognitive Impairment; Early Parkinson's disease; Machine Learning; Risk Model

Subject

Medicine and Pharmacology, Neuroscience and Neurology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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