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Prediction of safety accident subtypes for persons with dementia using sensors and machine learning: an observational
Eunjin Yang1, Ji Yeon Lee2, YeonKyu Choi3
1College of Nursing, Research Institute of AI and Nursing Science, Gachon University, Incheon, Republic of Korea.
Background And Objectives:
We explored the use of machine learning models for predicting safety accident subtypes among individuals with dementia using in-home sensors and to identify key predictors.
Research Design And Methods:
An observational study was conducted using 966 days of in-home sensor data, sleep data from wearable Actiwatch devices, caregiver-completed structured safety accident diary data, and individual data collected in South Korea. Five machine learning classification models were developed to predict physical injury, nighttime behaviors/wandering, and risky behaviors. Model performance was compared, and the most important predictive features were extracted.
Results:
The Gradient Boosting Machine showed the best performance in predicting physical injury and nighttime behaviors, while CatBoost performed best for risky behaviors. Activity patterns recorded using in-home sensors emerged as essential features for predicting different safety accident subgroups, particularly for nighttime behaviors and wandering.
Discussion And Implications:
These findings highlight the potential of these technologies to identify high-risk individuals with dementia. Further research is recommended to integrate these methods for daily safety monitoring of this population.
