Being alone after late-life divorce: freedom and loneliness from an intergenerational familial/dyadic perspective

Koren, Chaya; Cohen, Yafit; Demeter, Naor

doi:10.1080/13607863.2024.2377264

Cited by 2 publications

References 47 publications

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Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey

Lin,

Li,

Wang

et al. 2024

BMC Geriatr

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Background Loneliness is prevalent among the elderly and has intensified due to global aging trends. It adversely affects both mental and physical health. Traditional scales for measuring loneliness may yield biased results due to varying definitions. The advancements in machine learning offer new opportunities for improving the measurement and assessment of loneliness through the development of risk assessment models. Methods Data from the 2018 Chinese Longitudinal Healthy Longevity Survey, involving about 16,000 participants aged ≥ 65 years, were used. The study examined the relationships between loneliness and factors such as functional limitations, living conditions, environmental influences, age-related health issues, and health behaviors. Using R 4.4.1, seven assessment models were developed: logistic regression, ridge regression, support vector machines, K-nearest neighbors, decision trees, random forests, and multi-layer perceptron. Models were evaluated based on ROC curves, accuracy, precision, recall, F1 scores, and AUC. Results Loneliness prevalence among elderly Chinese was 23.4%. Analysis identified 15 evaluative factors and evaluated seven models. Multi-layer perceptron stands out for its strong nonlinear mapping capability and adaptability to complex data, making it one of the most effective models for assessing loneliness risk. Conclusion The study found a 23.4% prevalence of loneliness among elderly individuals in China. SHAP values indicated that marital status has the strongest evaluative value across all forecasting periods. Specifically, elderly individuals who are never married, widowed, divorced, or separated are more likely to experience loneliness compared to their married counterparts.

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Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey

Lin,

Li,

Wang

et al. 2024

BMC Geriatr

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Can Loneliness be Predicted? Development of a Risk Prediction Model for Loneliness among Elderly Chinese: A Study Based on CLHLS

Lin,

Li,

et al. 2024

Preprint

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Background: Loneliness is prevalent among the elderly, worsened by global aging trends. It impacts mental and physiological health. Traditional scales for measuring loneliness may be biased due to cognitive decline and varying definitions. Machine learning advancements offer potential improvements in risk prediction models. Methods: Data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), involving over 16,000 participants aged ≥65 years, were used. The study examined the relationships between loneliness and factors such as cognitive function, functional limitations, living conditions, environmental influences, age-related health issues, and health behaviors. Using R 4.4.1, seven predictive models were developed: logistic regression, ridge regression, support vector machines, K-nearest neighbors, decision trees, random forests, and multi-layer perceptron. Models were evaluated based on ROC curves, accuracy, precision, recall, F1 scores, and AUC. Results: Loneliness prevalence among elderly Chinese was 23.4%. Analysis identified 16 predictive factors and evaluated seven models. Logistic regression was the most effective model for predicting loneliness risk due to its economic and operational advantages. Conclusion: The study found a 23.4% prevalence of loneliness among elderly individuals in China. SHAP values indicated that higher MMSE scores correlate with lower loneliness levels. Logistic regression was the superior model for predicting loneliness risk in this population.

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Being alone after late-life divorce: freedom and loneliness from an intergenerational familial/dyadic perspective

Cited by 2 publications

References 47 publications

Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey

Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey

Can Loneliness be Predicted? Development of a Risk Prediction Model for Loneliness among Elderly Chinese: A Study Based on CLHLS

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