Development of Random Forest Algorithm Based Prediction Model of Alzheimer's Disease Using Neurodegeneration Pattern.

ABSTRACT:

Objective

Alzheimer's disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient's severity of neurodegeneration independent from the patient's clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).

Methods

We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation metho

SUBMITTER: Kim J 

PROVIDER: S-EPMC7897872 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature