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Improving Diagnosis of Alzheimer’s Disease by Data Fusion

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  • Zhanpan Zhang

    (GE Global Research, Niskayuna, USA)

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.

Suggested Citation

  • Zhanpan Zhang, 2023. "Improving Diagnosis of Alzheimer’s Disease by Data Fusion," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 49(5), pages 41051-41059, April.
    • Handle: RePEc:abf:journl:v:49:y:2023:i:5:p:41051-41059
    DOI: 10.26717/BJSTR.2023.49.007866
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