Leisure and Happiness of the Elderly: A Machine Learning Approach
Abstract
:1. Introduction
2. Literature Review
2.1. Concept of Happiness and Factors Affecting Happiness
2.2. Leisure and Happiness of the Elderly
2.3. Leisure Factors Affecting the Happiness of the Elderly
3. Research Methods
3.1. Research Model
3.2. Data Analysis and Target Population
- Survey structure
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- Survey agency: Ministry of Culture, Sports and Tourism
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- Survey subject: Population over 15 years old nationwide
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- Number of valid respondents: 10,060
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- Survey period: 9 September 2019–14 November 2019
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- Survey method: Household visit interviews in which a professional surveyor visits a sample of households and fills in the responses to the questionnaire
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- Legal basis: Statistics of approval designated by the National Statistics Agency (approval number: No. 113014)
- Sampling design
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- Target population: Population over 15 years old nationwide
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- Survey population: Members of households over the age of 15 who reside in all households in Korea at the time of the survey
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- Sampling frame: Use of data from the National Statistics Office’s ‘2017 Census’ survey
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- Stratification: A total of 17 cities and provinces are stratified into urban cities and rural areas which divided into dong (i.e., towns) and eup/myeon (i.e., villages), reflecting the characteristics of urban and rural areas.
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- Sampling method: Stratified Multi-Stage Cluster Sampling
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- Sample allocation and extraction method: Random extraction after allocation by square root proportional distribution in consideration of the number of households in the trial by the precision and adequacy of the sample, etc.
- Weighting
3.3. Analytic Variables
3.3.1. Result Variable
3.3.2. Predictor Variables
3.4. Analysis Method
3.4.1. Data Preprocessing
One-Hot Encoding
Data Scaling
Multicollinearity
Data Partitioning
Building and Evaluating Predictive Models
Hyperparameter Optimization
Analysis Tools
4. Research Results
4.1. Model Performance Evaluation
4.2. Hyperparameter Optimization
4.3. Importance of Predictor Variables
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Variable Type | Item | Value |
---|---|---|---|
q2_1_n2_2 | categorical variable | Type of leisure activity in which the most participated in the past year | 1 = Cultural and artistic activity 2 = Sports activity 3 = Tourism 4 = Hobbies 5 = Rest activity 6 = Social and other activities |
q2_2_1_1 | categorical variable | Companionship (leisure activity companion) | 1 = Alone (without a companion) 2 = Together (with a companion) |
q2_3_1_1 | categorical variable | Frequency in leisure activities | 1 = Everyday 2 = Not everyday |
q2_5_1 | continuous variable | Leisure cost (one-time basis) | Response value (won) |
q9 | continuous variable | Leisure cost (monthly average) | Response value (won) |
q13 | continuous variable | Average leisure time per day | Response value (hour) |
q16_1 | categorical variable | The most used leisure space outside of the home | 1 = Indoor space 2 = Outdoor space |
q19 | categorical variable | Use of public leisure facilities | 1 = Yes 2 = No |
q22 | categorical variable | Participation in club activities | 1 = Yes 2 = No |
q23 | categorical variable | Volunteer activity | 1 = Yes 2 = No |
q24 | continuous variable | Time spent on smart devices during leisure time | Response value (hour) |
q27 | continuous variable | Leisure policy satisfaction | 7-point scale 1 = Very dissatisfied 7 = Very satisfied |
q30 | continuous variable | Overall leisure life satisfaction | 7-point scale 1 = Very dissatisfied 7 = Very satisfied |
q35 | continuous variable | Leisure activity constraints (degree) | 7-point scale 1 = No constraints at all 7 = Very constrained |
Variable | Baseline Variables |
---|---|
q2_1_n2_2 | Rest activity |
q2_2_1_1 | Alone |
q2_3_1_1 | Everyday |
q16_1 | Indoor space |
q19 | Yes |
q22 | Yes |
q23 | Yes |
Model | RMSE | R2 |
---|---|---|
Linear Regression | 1.202 | 0.311 |
Ridge | 1.201 | 0.312 |
Lasso | 1.449 | 0.000 |
SVM | 1.200 | 0.314 |
Decision Tree | 1.638 | −0.278 |
Random Forest | 1.217 | 0.294 |
XGBoost | 1.351 | 0.130 |
LightGBM | 1.278 | 0.222 |
Model | Hyperparameter Optimzation | RMSE | R2 |
---|---|---|---|
Linear Regression | - | 1.202 | 0.311 |
Ridge | alpha = 50 | 1.198 | 0.316 |
Lasso | alpha = 0.01 | 1.195 | 0.319 |
SVM | kernel = ‘linear’, C = 0.1, gamma = 0.001 | 1.198 | 0.316 |
Decision Tree | max_depth = 3, min_samples_leaf = 45 | 1.225 | 0.284 |
Random Forest | n_estimators = 170 | 1.207 | 0.305 |
XGBoost | colsample_bylevel = 0.5, colsample_bytree = 0.6, gamma = 0.1, learning_rate = 0.01, max_depth = 3, min_child_weight = 5, n_estimators = 500, reg_lambda = 0.1, subsample = 0.8 | 1.195 | 0.319 |
LightGBM | colsample_bytree = 0.3, learning_rate = 0.03, max_depth = 3, n_estimators = 300, num_leaves = 5, reg_alpha = 0.007, reg_lambda = 0.2 | 1.197 | 0.317 |
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Kim, E.-J.; Kang, H.-W.; Park, S.-M. Leisure and Happiness of the Elderly: A Machine Learning Approach. Sustainability 2024, 16, 2730. https://doi.org/10.3390/su16072730
Kim E-J, Kang H-W, Park S-M. Leisure and Happiness of the Elderly: A Machine Learning Approach. Sustainability. 2024; 16(7):2730. https://doi.org/10.3390/su16072730
Chicago/Turabian StyleKim, Eui-Jae, Hyun-Wook Kang, and Seong-Man Park. 2024. "Leisure and Happiness of the Elderly: A Machine Learning Approach" Sustainability 16, no. 7: 2730. https://doi.org/10.3390/su16072730