Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-ß oligomerization data.
BMC Med Inform Decis Mak
; 22(1): 286, 2022 11 07.
Article
em En
| MEDLINE
| ID: mdl-36344984
ABSTRACT
BACKGROUND:
The tendency of amyloid-ß to form oligomers in the blood as measured with Multimer Detection System-Oligomeric Amyloid-ß (MDS-OAß) 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-OAß and to develop machine learning algorithms to predict amyloid positron emission tomography (PET) positivity.METHODS:
The performance of EDTA-based MDS-OAß in predicting PET positivity was evaluated in 312 individuals with various machine learning models. The models with various combinations of features (i.e., MDS-OAß 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-OAß 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-OAß, MMSE, Age, and APOE. The Support Vector Machine using the MDS-OAß 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-OAß combined with the MMSE and apolipoprotein E status can be used to prescreen for amyloid PET positivity.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença de Alzheimer
/
Disfunção Cognitiva
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Med Inform Decis Mak
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article