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dc.contributor.supervisorIfeachor, Emmanuel
dc.contributor.authorEke, Chima Stanley
dc.contributor.otherSchool of Engineering, Computing and Mathematicsen_US
dc.date.accessioned2022-05-20T14:38:24Z
dc.date.issued2022
dc.identifier10602882en_US
dc.identifier.urihttp://hdl.handle.net/10026.1/19245
dc.description.abstract

Alzheimer’s disease (AD) is a neurodegenerative disease with typical clinical symptoms in the form of progressive cognitive impairment and memory loss. To facilitate early diagnosis of AD and a greater understanding of the mechanisms underlying its clinical expression, the use of biomarkers is necessary. Furthermore, it is believed that biomarkers provide a more objective and accessible means of diagnosis. Currently, established biomarkers include neuroimaging markers, such as those based on positron emission tomography (PET), and biochemical markers such as cerebrospinal fluid (CSF) markers. However, neuroimaging is expensive and may not be widely available and CSF testing is invasive. Blood-based biomarkers offer the potential for the development of minimally invasive, low-cost and time-efficient methods for AD detection to complement CSF and neuroimaging. In this work, a data-driven approach, machine learning in particular, was exploited to identify blood-based biomarker panels consisting of a few markers (as no single marker provides sufficient performance) that may serve as screening tools in a multi-stage diagnostic procedure. Novel contributions were made in biomarker discovery, including identification of novel panels as well as panel selection procedures that emphasize performance and robustness. Identified biomarker panels have remarkable classification performance at discriminating between Alzheimer’s dementia as well as mild cognitive impairment subjects and normal controls. Another set of identified blood-based biomarkers could classify individuals with abnormal/normal levels of CSF amyloid β42, which is one of the key early markers of AD. Furthermore, a novel software prototype was developed to demonstrate the possible clinical use of identified biomarker panels. A significance of this work is its potential contribution to the development of rapid testing and cost-effective point of care devices to facilitate AD diagnosis.

en_US
dc.language.isoen
dc.publisherUniversity of Plymouth
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectAlzheimer's Diseaseen_US
dc.subjectBiomarkeren_US
dc.subjectBlood Biomarkeren_US
dc.subjectDiagnosisen_US
dc.subjectMachine Learningen_US
dc.subjectScreening Toolen_US
dc.subjectPoint of care deviceen_US
dc.subject.classificationPhDen_US
dc.titleMACHINE LEARNING-BASED EXPLORATION OF BLOOD-BASED BIOMARKERS FOR ALZHEIMER’S DISEASE DIAGNOSISen_US
dc.typeThesis
plymouth.versionpublishableen_US
dc.identifier.doihttp://dx.doi.org/10.24382/1259
dc.identifier.doihttp://dx.doi.org/10.24382/1259
dc.rights.embargodate2023-05-20T14:38:24Z
dc.rights.embargoperiod12 monthsen_US
dc.type.qualificationDoctorateen_US
rioxxterms.funderHorizon 2020en_US
rioxxterms.identifier.projectBlood biomarker-based diagnostic tools for Early-stage Alzheimer's disease (BBdiag), grant no. 721281en_US
rioxxterms.versionNA
plymouth.orcid.id0000-0002-3095-357Xen_US


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Attribution 3.0 United States
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