Table 5 The result of evaluating the efficiency of ML models
From: Prediction of successful aging using ensemble machine learning algorithms
Model | Precision | Recall | Specificity | F-measure | Accuracy | AUC |
|---|---|---|---|---|---|---|
DT | 74 | 73.2 | 73.7 | 73.6 | 73.5 | 80 |
95% CI | (0.72, 0.752) | (0.725,0.74) | (0.719,0.75.2) | (0.71,0.747) | (0.71,0.759) | (0.79, 0.813) |
Stanandard deviation (SD) | 0.041 | 0.013 | 0.029 | 0.038 | 0.0217 | 0.029 |
SVM | 78 | 97.5 | 81.6 | 86.3 | 85.1 | 95 |
95% CI | (0.763, 0.791) | (0.763,0.78) | (0.792,0.83) | (0.839,0.884) | (0.845,0.87) | (0.937, 0.961) |
SD | 0.035 | 0.019 | 0.035 | 0.102 | 0.042 | 0.018 |
NB | 65 | 70.6 | 67.6 | 67.6 | 68.6 | 74 |
95% CI | (0.631, 0.67) | (0.69,0.713) | (0.659,0.692) | (0.658,0.685) | (0.669,0.69) | (0.725, 0.763) |
SD | 0.024 | 0.022 | 0.030 | 0.0173 | 0.015 | 0.037 |
ANN | 48 | 64.7 | 77.2 | 89.3 | 77.1 | 78.2 |
95% CI | (0.462, 0.499) | (0.615,0.67) | (0.764,0.788) | (0.883,0.907) | (0.759,0.78) | (0.761, 0.793) |
SD | 0.06 | 0.057 | 0.035 | 0.041 | 0.012 | 0.02 |
KNN | 90 | 72.1 | 86.6 | 80 | 73.3 | 91 |
95% CI | (0.886, 0.914) | (0.715,0.73) | (0.852,0.887) | (0.780,0.817) | (0.715,0.74) | (0.89, 0.925) |
SD | 0.048 | 0.019 | 0.051 | 0.0227 | 0.018 | 0.012 |
Ensemble 1 (KNN) | 93 | 87.8 | 92.4 | 90.3 | 89.6 | 96 |
95% CI | (0.917, 0.941) | (0.86,0.893) | (0.919,0.941) | (0.89,0.917) | (0.874,0.91) | (0.951, 0.973) |
SD | 0.03 | 0.024 | 0.0107 | 0.0162 | 0.052 | 0.027 |
Ensemble 2 (Bag Tree) | 82 | 86.3 | 82.8 | 85.8 | 84.4 | 90 |
95% CI | (0.802, 0.841) | (0.851,0.87) | (0.812,0.845) | (0.832,0.871) | (0.83,0.861) | (0.891, 0.817) |
SD | 0.03 | 0.012 | 0.031 | 0.026 | 0.039 | 0.032 |