Journal of Preventive Medicine and Public Health

Original Articles

Development of Machine Learning Models to Categorize Life Satisfaction in Older Adults in Korea: Suyeong Bae, Mi Jung Lee, Ickpyo Hong; J Prev Med Public Health. 2025;58(2):127-135. Published online October 23, 2024; DOI: https://doi.org/10.3961/jpmph.24.324

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Abstract Summary PDF: 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.; 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.

Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques: Similien Ndagijimana, Ignace Habimana Kabano, Emmanuel Masabo, Jean Marie Ntaganda; J Prev Med Public Health. 2023;56(1):41-49. Published online January 6, 2023; DOI: https://doi.org/10.3961/jpmph.22.388

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408 Download
7 Web of Science
12 Crossref

Objectives
Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children.
Methods
The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen.
Results
The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model’s ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother’s height, television, the child’s age, province, mother’s education, birth weight, and childbirth size were the most important predictors of stunting status.
Conclusions
Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

Summary

Citations

Citations to this article as recorded by

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PLOS ONE.2025; 20(1): e0316452. CrossRef
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