pISSN 1226-6329 / eISSN 2733-4600
대한노인정신의학회 (28권2호 33-40)
Machine Learning-Based Multi-Modal Prediction of Cognitive Decline in Community-Dwelling Older Adults
Machine Learning-Based Multi-Modal Prediction of Cognitive Decline in Community-Dwelling Older Adults
Jinhak Kim, MD, Narae Kim, MA, Bumhee Park, PhD, Hyun Woong Roh, MD, PhD, Chang Hyung Hong, MD, PhD, Sang Joon Son, MD, PhD; and Research Committee of Korean Association for Geriatric Psychiatry
Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Departments of Biomedical Informatics and Psychiatry, Ajou University School of Medicine, Suwon, Korea
Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older
adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to enhance early detection of cognitive decline.
Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with
Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance imaging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05
points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial assessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score,
while Shapley additive explanation values were used to assess feature importance.
Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor
of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction.
Conclusion: This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a
scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.
Cognitive dysfunction; Dementia; Machine learning; Geriatrics.
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