- Development of Machine Learning Models to Categorize Life Satisfaction in Older Adults in Korea
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Suyeong Bae, Mi Jung Lee, Ickpyo Hong
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J Prev Med Public Health. 2025;58(2):127-135. Published online October 23, 2024
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DOI: https://doi.org/10.3961/jpmph.24.324
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 Abstract
Summary
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- Objectives
This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone.
Methods
Data were extracted from 3112 older adults participating in the 2020 Korea Senior Survey. We employed 5 ML models to classify the life satisfaction of older adults living alone: logistic Lasso regression, decision tree-based classification and regression tree (CART), C5.0, random forest, and extreme gradient boost (XGBoost). The variables used as predictors included demographics, health status, functional abilities, environmental factors, and activity participation. The performance of these ML models was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, we assessed the significance of variable importance as indicated by the final classification models.
Results
Out of the 1411 older adults living alone, 45.3% expressed satisfaction with their lives. The XGBoost model surpassed the performance of other models, achieving an F1-score of 0.72 and an AUC of 0.75. According to the XGBoost model, the five most important variables influencing life satisfaction were overall community satisfaction, self-rated health, opportunities to interact with neighbors, proximity to a child, and satisfaction with residence.
Conclusions
Overall satisfaction with the community environment emerged as the most significant predictor of life satisfaction among older adults living alone. These findings indicate that enhancing the supportiveness of the community environment could improve life satisfaction for this demographic.
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Summary
Korean summary
본 연구는 2020년 노인실태조사에 참여한 3,112명의 독거노인 데이터를 활용하여 이들의 삶의 만족도를 분류하는 머신러닝 모델을 개발하였다. 아울러, 해당 모델을 통해 독거노인의 삶의 만족도 분류에 영향을 미치는 주요 변수를 도출하였다. 본 연구는 독거노인의 삶의 만족도 향상을 위해 고려해야 할 핵심 요인들을 제시한다는 점에서 의의가 있다.
Key Message
This study developed a machine learning model to classify life satisfaction among 3,112 older adults living alone, based on data from the 2020 Korea Senior Survey. Furthermore, the study identified key variables that contribute to the classification of life satisfaction in this population. These findings provide insights into important factors that should be considered to enhance the life satisfaction of older adults living alone.
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