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Media type:
E-Article
Title:
Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data
Contributor:
Youn, Young Chul;
Kim, Hye Ryoun;
Shin, Hae-Won;
Jeong, Hae-Bong;
Han, Sang-Won;
Pyun, Jung-Min;
Ryoo, Nayoung;
Park, Young Ho;
Kim, SangYun
Published:
Springer Science and Business Media LLC, 2022
Published in:
BMC Medical Informatics and Decision Making, 22 (2022) 1
Language:
English
DOI:
10.1186/s12911-022-02024-z
ISSN:
1472-6947
Origination:
Footnote:
Description:
AbstractBackgroundThe tendency of amyloid-β to form oligomers in the blood as measured with Multimer Detection System-Oligomeric Amyloid-β (MDS-OAβ) is a valuable biomarker for Alzheimer’s disease and has been verified with heparin-based plasma. The objective of this study was to evaluate the performance of ethylenediaminetetraacetic acid (EDTA)-based MDS-OAβ and to develop machine learning algorithms to predict amyloid positron emission tomography (PET) positivity.MethodsThe performance of EDTA-based MDS-OAβ in predicting PET positivity was evaluated in 312 individuals with various machine learning models. The models with various combinations of features (i.e., MDS-OAβ level, age, apolipoprotein E4 alleles, and Mini-Mental Status Examination [MMSE] score) were tested 50 times on each dataset.ResultsThe random forest model best-predicted amyloid PET positivity based on MDS-OAβ combined with other features with an accuracy of 77.14 ± 4.21% and an F1 of 85.44 ± 3.10%. The order of significance of predictive features was MDS-OAβ, MMSE, Age, and APOE. The Support Vector Machine using the MDS-OAβ value only showed an accuracy of 71.09 ± 3.27% and F−1 value of 80.18 ± 2.70%.ConclusionsThe Random Forest model using EDTA-based MDS-OAβ combined with the MMSE and apolipoprotein E status can be used to prescreen for amyloid PET positivity.