Development of Random Forest Algorithm Based Prediction Model of Alzheimer’s Disease Using Neurodegeneration Pattern
- Author:
JeeYoung KIM
1
;
Minho LEE
;
Min Kyoung LEE
;
Sheng-Min WANG
;
Nak-Young KIM
;
Dong Woo KANG
;
Yoo Hyun UM
;
Hae-Ran NA
;
Young Sup WOO
;
Chang Uk LEE
;
Won-Myong BAHK
;
Donghyeon KIM
;
Hyun Kook LIM
Author Information
- Publication Type:Original Article
- From:Psychiatry Investigation 2021;18(1):69-79
- CountryRepublic of Korea
- Language:English
-
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 method compared with the Freesurfer’s which is a commonly used segmentation software.
Results:Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer’s (6–8 hours).
Conclusion:Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.
