Improving Diagnosis of Alzheimer’s Disease by Data Fusion

Zhanpan Zhang
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Zhanpan Zhang: GE Global Research, Niskayuna, USA

Biomedical Journal of Scientific & Technical Research, 2023, vol. 49, issue 5, 41051-41059

Abstract: Blood-based protein biomarkers predicting brain amyloid burden would have great utility for the enrichment of Alzheimer’s Disease (AD) clinical trials, including large-scale prevention trials. In this paper, we adopt data fusion to combine multiple high dimensional data sets upon which classification models are developed to predict amyloid burden as well as the clinical diagnosis. Specifically, non-parametric techniques are used to pre-select variables, and random forest and multinomial logistic regression techniques with LASSO penalty are performed to build classification models. We apply the proposed data fusion framework to the AIBL imaging cohort and demonstrate improvement of the clinical status classification accuracy. Furthermore, variable importance is evaluated to discover potential novel biomarkers associated with AD.

Keywords: Journals on Medical Drug and Therapeutics; Journals on Emergency Medicine; Physical Medicine and Rehabilitation; Journals on Infectious Diseases Addiction Science and Clinical Pathology; Open Access Clinical and Medical Journal; Journals on Biomedical Science; List of Open Access Medical Journal; Journals on Biomedical Engineering; Open Access Medical Journal; Biomedical Science Articles; Journal of Scientific and Technical Research (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:abf:journl:v:49:y:2023:i:5:p:41051-41059

DOI: 10.26717/BJSTR.2023.49.007866

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