Machine learning application for classification of Alzheimer's disease stages using ¹⁸F-flortaucipir positron emission tomography.

ABSTRACT:

Background

The progression of Alzheimer's dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from ¹⁸F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley

SUBMITTER: Park SW 

PROVIDER: S-EPMC10149022 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature