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KMID : 1118520210180010069
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Psychiatry Investigation
2021 Volume.18 No. 1 p.69 ~ p.79
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Development of Random Forest Algorithm Based Prediction Model of Alzheimer’s Disease Using Neurodegeneration Pattern
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Kim Jee-Young
Lee Min-Ho
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 Dong-Hyeon
Lim Hyun-Kook
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Abstract
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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.
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KEYWORD
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Random forest, Alzheimer’s disease, Mild cognitive impairment, Segmentation, MRI
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