Table 6 PSO model performance across different pain types.
From: Classification of musculoskeletal pain using machine learning
Pain type | Accuracy | Precision | Recall | F1 score | AUC score | Time consumed (s) |
|---|---|---|---|---|---|---|
Neck pain | 0.960 | 0.959 | 0.956 | 0.957 | 0.956 | 42.450 |
Shoulder pain | 0.958 | 0.960 | 0.955 | 0.957 | 0.955 | 37.062 |
Elbow pain | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 45.411 |
Wrist pain | 0.963 | 0.962 | 0.957 | 0.959 | 0.957 | 36.654 |
Thoracic spine pain | 0.977 | 0.979 | 0.975 | 0.977 | 0.975 | 44.924 |
Low back pain | 0.972 | 0.969 | 0.974 | 0.971 | 0.974 | 40.364 |
Hip pain | 0.997 | 0.998 | 0.992 | 0.995 | 0.992 | 45.738 |
Knee pain | 0.958 | 0.960 | 0.950 | 0.954 | 0.950 | 39.395 |
Ankle pain | 0.989 | 0.988 | 0.980 | 0.984 | 0.980 | 43.029 |
