Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia - model development and validation

Aug 5, 2026

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Behavioral and psychological symptoms of dementia (BPSD) are a difficult, costly component of dementia care. This study developed and validated machine learning models to predict BPSD onset in community-dwelling older adults with dementia using baseline demographic and health data, premorbid personality traits, and actigraphy to monitor sleep and activity. A caregiver symptom diary captured daily occurrences of 12 BPSD symptoms across seven subsyndromes, evaluated with logistic regression, random forest, gradient boosting, and support vector machine.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Model development and validation

Participants and data collection

BPSD symptom classification and triggers

Predictive modeling approaches

Model performance and key features

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Machine learning‑based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation Eunhee Cho1, Sujin Kim2, Seok‑Jae Heo 3, Jinhee Shin4, Sinwoo Hwang5, Eunji Kwon 5, SungHee Lee6, SangGyun Kim6 & Bada Kang1* The behavioral and psychological symptoms of dementia (BPSD) are challenging aspects of dementia care. This study used machine learning models to predict the occurrence of BPSD among community‑dwelling older adults with dementia. We included 187 older adults with dementia for model training and 35 older adults with dementia for external validation. Demographic and health data and premorbid personality traits were examined atthe baseline, and actigraphy was utilized to monitor sleep and activity levels. A symptom diary tracked caregiver‑perceived symptom triggersand the daily occurrence of 12 BPSD classified into seven subsyndromes. Several prediction models were also employed, including logistic regression, random forest, gradient boosting machine, and support vector machine. The random forest models revealed the highest area under the receiver operating characteristic curve (AUC) values for hyperactivity, euphoria/elation, and appetite and eating disorders; the gradient boosting machine models for psychotic and affective symptoms; and the support vector machine model showed the highest AUC. The gradient boosting machine model achieved the best performance in terms of average AUC scores across the seven subsyndromes. Caregiver‑perceived triggers demonstrated higher feature importance values across the seven subsyndromes than other features. Our findings demonstrate the possibility of predicting BPSD using a machine learning approach. The number of people living with dementia is estimated to be more than 50 million worldwide; with the increasing aging population, this number is expected to triple by 20501. Behavioral and psychological symptoms of dementia (BPSD), also known as neurocognitive symptoms, are a heterogeneous group of non-cognitive symptoms and behaviors such as agitation, aggression, apathy, and depression that manifest in individuals diagnosed with dementia2. Almost 90% of individuals with dementia at all stages and etiologies are affected by these symptoms3. They are increasingly recognized as the most complex, challenging, and costly aspects of dementia care4, and result in decreased independence in activities of daily living and quality of life5, nursing home placement6, healthcare utilization7, and increased caregiver burden and depression8 for individuals with dementia. The factors that have been identified as contributing to BPSD can be categorized as (1) those related to individuals with dementia, such as dementia-related neurobiological factors, unmet needs, and premorbid personality; (2) those related to caregivers, such as communication approach; and (3) environmental triggers such as lack of stimuli and environmental change9. They cause symptoms independently or in combination with other aspects9. Disturbances in circadian rhythm, including impaired sleep and inadequate physical activity, have also been identified as stressors that can trigger BPSD10, 11. Recent empirical research has identified an association 1Mo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul 03722, Republic of Korea. 2Department of Nursing, Yong-In Arts and Science University, Gyeonggi-do, Korea. 3Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea. 4College of Nursing, Woosuk University, Jeollabuk-do, Korea. 5Korea Armed Forces Nursing Academy, Daejeon, Korea. 6BRFrame Inc, Seoul, Korea. * email: [bdkang@yuhs.ac](bdkang@yuhs.ac) [www. nature.com/scientificreports/](www. nature.com/scientificreports/) between disruption of circadian rhythms

FAQs

What is the primary goal of the machine learning models in this study?

To predict the occurrence of behavioral and psychological symptoms of dementia (BPSD) among community-dwelling older adults with dementia.

How were sleep and activity levels measured for the predictive models?

Actigraphy was used to monitor sleep and activity levels continuously in everyday conditions.

Which machine learning approach performed best across the seven BPSD subsyndromes?

The gradient boosting machine model achieved the best overall performance based on average AUC scores across the seven subsyndromes.

Aug 5, 2026

Upload by Finn