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

Background: The tendency of amyloid-β oligomer (AβO) formation in the blood, measured with Multimer Detection System-Oligomeric Amyloid-β (MDS-OAβ), is a valuable biomarker for Alzheimer’s disease (AD), which has been verified using 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 showed best performance in MDS-OAβ predicting amyloid PET positivity with other features showed an accuracy of 77.14±4.21% and a F1-value of 85.44±3.10 %. The order of significance in contributing 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.