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A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer’s Disease Spectrum

Massetti, Noemi and Russo, Mirella and Franciotti, Raffaella and Nardini, Davide and Mandolini, Giorgio and Granzotto, Alberto and Bomba, Manuela and Delli Pizzi, Stefano and Mosca, Alessandra and Scherer, Reinhold and Onofrj, Marco and Sensi, Stefano L (2022) 'A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer’s Disease Spectrum.' Journal of Alzheimer's Disease, 85 (4). pp. 1639-1655. ISSN 1387-2877

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Abstract

Background: Alzheimer’s disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD. Objective: To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Alzheimer’s Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion. Methods: We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables. Results: The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects. Conclusion: Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

Item Type: Article
Uncontrolled Keywords: Alzheimer’s disease; conversion; dementia; machine learning; mild cognitive impairment; random forest
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 17 Jan 2022 15:35
Last Modified: 28 Mar 2022 13:13
URI: http://repository.essex.ac.uk/id/eprint/32020

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