Table 4 Machine learning classification of patients with MCI.
Dataset | Algorithm for classification | Algorithm for feature reduction | No. of features reduced | AUC (95% CI) | ACC (95% CI) | Recall (95% CI) | Precision (95% CI) | F1 (95% CI) |
|---|---|---|---|---|---|---|---|---|
Gait + Sleep + BC | SVM | PCA | 40 | 0.94 (0.91–0.97) | 0.80 (0.75–0.85) | 0.86 (0.80–0.92) | 0.69 (0.58–0.80) | 0.75 (0.70–0.80) |
Gait + Sleep + BC | RF | PCA | 40 | 0.87 (0.81–0.93) | 0.76 (0.69–0.83) | 0.74 (0.58–0.90) | 0.65 (0.58–0.72) | 0.67 (0.61–0.73) |
Gait + Sleep + BC | MLP | ICA | 60 | 0.94 (0.90–0.98) | 0.78 (0.73–0.83) | 0.94 (0.90–0.98) | 0.63 (0.60–0.66) | 0.75 (0.72–0.78) |
Gait + Sleep + BC | CNN | PCA | 60 | 0.94 (0.91–0.97) | 0.79 (0.74–0.84) | 0.86 (0.80–0.92) | 0.70 (0.64–0.76) | 0.75 (0.70–0.80) |
GM + WMH | SVM | ICA | 20 | 0.90 (0.86–0.94) | 0.81 (0.77–0.85) | 0.82 (0.72–0.92) | 0.71 (0.65–0.77) | 0.75 (0.71–0.79) |
GM + WMH | RF | ICA | 60 | 0.85 (0.79–0.91) | 0.78 (0.72–0.84) | 0.73 (0.69–0.77) | 0.70 (0.64–0.76) | 0.70 (0.67–0.73) |
GM + WMH | MLP | ICA | 40 | 0.90 (0.87–0.93) | 0.81 (0.77–0.85) | 0.90 (0.86–0.94) | 0.69 (0.65–0.73) | 0.78 (0.74–0.82) |
GM + WMH | CNN | ICA | 40 | 0.94 (0.90–0.98) | 0.86 (0.83–0.89) | 0.87 (0.83–0.91) | 0.80 (0.73–0.87) | 0.82 (0.79–0.85) |
GM + WMH + Gait + Sleep + BC | SVM | ICA | 20 | 0.93 (0.89–0.97) | 0.86 (0.82–0.90) | 0.85 (0.79–0.91) | 0.82 (0.70–0.94) | 0.81 (0.78–0.84) |
GM + WMH + Gait + Sleep + BC | RF | PCA | 20 | 0.92 (0.88–0.96) | 0.81 (0.76–0.86) | 0.79 (0.75–0.83) | 0.74 (0.67–0.81) | 0.75 (0.71–0.79) |
GM + WMH + Gait + Sleep + BC | MLP | ICA | 40 | 0.94 (0.92–0.96) | 0.86 (0.82–0.90) | 0.82 (0.74–0.90) | 0.82 (0.76–0.88) | 0.80 (0.77–0.83) |
GM + WMH + Gait + Sleep + BC | CNN | PCA | 60 | 0.93 (0.89–0.97) | 0.89 (0.85–0.93) | 0.78 (0.70–0.86) | 0.91 (0.87–0.95) | 0.83 (0.80–0.86) |
