Table 4.
Comparison of performance metrics of different machine learning models.
| Accuracy | Precision | Recall | F1-Score | |
|---|---|---|---|---|
| Train set | ||||
| Random Forest | 0.74 | 0.82 | 0.74 | 0.76 |
| XGBoost | 0.73 | 0.76 | 0.73 | 0.74 |
| SVM | 0.76 | 0.79 | 0.76 | 0.77 |
| Neural Network | 0.76 | 0.76 | 0.76 | 0.75 |
| Test set | ||||
| Random Forest | 0.75 | 0.82 | 0.75 | 0.77 |
| XGBoost | 0.75 | 0.76 | 0.75 | 0.76 |
| SVM | 0.68 | 0.69 | 0.68 | 0.69 |
| Neural Network | 0.71 | 0.71 | 0.71 | 0.70 |
Weighted average results; target variable: total PA higher or lower than 600 MET·min/week; features included in the model involved age categories, sex, BMI, crowding index, education, marital status, job location during lockdown, auto-declared PA change during lockdown, type of PA during lockdown, employment status, and sitting time.