Cargando…

Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data

BACKGROUND: The 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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641916/
https://www.ncbi.nlm.nih.gov/pubmed/36344984
http://dx.doi.org/10.1186/s12911-022-02024-z
_version_ 1784826189784809472
author 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
author_facet 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
author_sort Youn, Young Chul
collection PubMed
description BACKGROUND: The 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. METHODS: The 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. RESULTS: The 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%. CONCLUSIONS: The 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.
format Online
Article
Text
id pubmed-9641916
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-96419162022-11-15 Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data 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 BMC Med Inform Decis Mak Research BACKGROUND: The 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. METHODS: The 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. RESULTS: The 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%. CONCLUSIONS: The 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. BioMed Central 2022-11-07 /pmc/articles/PMC9641916/ /pubmed/36344984 http://dx.doi.org/10.1186/s12911-022-02024-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
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
Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data
title Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data
title_full Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data
title_fullStr Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data
title_full_unstemmed Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data
title_short Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data
title_sort prediction of amyloid pet positivity via machine learning algorithms trained with edta-based blood amyloid-β oligomerization data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641916/
https://www.ncbi.nlm.nih.gov/pubmed/36344984
http://dx.doi.org/10.1186/s12911-022-02024-z
work_keys_str_mv AT younyoungchul predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata
AT kimhyeryoun predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata
AT shinhaewon predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata
AT jeonghaebong predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata
AT hansangwon predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata
AT pyunjungmin predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata
AT ryoonayoung predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata
AT parkyoungho predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata
AT kimsangyun predictionofamyloidpetpositivityviamachinelearningalgorithmstrainedwithedtabasedbloodamyloidboligomerizationdata