1. Development of Random Forest Algorithm Based Prediction Model of Alzheimer’s Disease Using Neurodegeneration Pattern
- Author
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Kim, JeeYoung, Lee, Minho, Lee, Min Kyoung, Wang, Sheng-Min, Kim, Nak-Young, Kang, Dong Woo, Um, Yoo Hyun, Na, Hae-Ran, Woo, Young Sup, Lee, Chang Uk, Bahk, Won-Myong, Kim, Donghyeon, and Lim, Hyun Kook
- Subjects
medicine.medical_specialty ,Patient characteristics ,Disease ,03 medical and health sciences ,Segmentation ,0302 clinical medicine ,Physical medicine and rehabilitation ,medicine ,Dementia ,Brain segmentation ,Biological Psychiatry ,030304 developmental biology ,0303 health sciences ,business.industry ,Neurodegeneration ,Mild cognitive impairment ,Cognition ,medicine.disease ,Random forest ,Psychiatry and Mental health ,Original Article ,business ,Alzheimer’s disease ,030217 neurology & neurosurgery ,MRI - 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 (
- Published
- 2021