Psychiatry Investig.
2021 Jan;18(1):69-79. 10.30773/pi.2020.0304.
Development of Random Forest Algorithm Based Prediction Model of Alzheimer’s Disease Using Neurodegeneration Pattern
- Affiliations
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- 1Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- 2Research Institute, NEUROPHET Inc., Seoul, Korea
- 3Department of Radiology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- 4Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- 5Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- 6Department of Psychiatry, St. Vincent’s Hospital Seoul, College of Medicine, The Catholic University of Korea, Suwon, Korea
- KMID: 2511581
- DOI: http://doi.org/10.30773/pi.2020.0304
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.
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