Machine Learning models for detection and assessment of progression in Alzheimer's disease based on blood and cerebrospinal fluid biomarkers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Machine-learning techniques were applied to human blood plasma and cerebrospinal fluid (CSF) biomarker data related to cognitive decline in Alzheimer’s Disease (AD) patients available via Alzheimer Disease Neuroimaging Initiative (ADNI) study. We observed the accuracy of AD diagnosis is greatest when protein biomarkers from cerebrospinal fluid are combined with plasma proteins using Support Vector Machines (SVM); this is not improved by adding age and sex. The area under the receiver operator characteristic (ROC) curve for our model of AD diagnosis based on a full (unbiased) set of plasma proteins was 0.94 in cross-validation and 0.82 on an external validation (test) set. Taking plasma in combination with CSF, the model reaches 0.98 area under the ROC curve on the test set. Accuracy of prediction of risk of mild cognitive impairment progressing to AD is the same for blood plasma biomarkers as for CSF and is not improved by combining them or adding age and sex as covariates.Clinical relevance— The identification of accurate and cost-effective biomarkers to screen for risk of developing AD and monitoring its progression is crucial for improved understanding of its causes and stratification of patients for treatments under development. This paper demonstrates the feasibility of AD detection and prognosis based on blood plasma biomarkers.
Original languageEnglish
Title of host publicationProccedings of the 45th Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
PublisherIEEE
Number of pages4
ISBN (Electronic)979-8-3503-2447-1
ISBN (Print)979-8-3503-2448-8
DOIs
Publication statusPublished - 11 Dec 2023
EventThe 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2023 - Sydney, Australia
Duration: 24 Jul 202327 Jul 2023
Conference number: 45
https://embc.embs.org/2023/

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
ISSN (Print)2375-7477
ISSN (Electronic)2694-0604

Conference

ConferenceThe 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2023
Abbreviated titleEMBC 2023
Country/TerritoryAustralia
CitySydney
Period24/07/2327/07/23
Internet address

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