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Table 1 Comparison of alternative machine learning methods. (LGB: Light Gradient Boosting, XGB: Extreme Gradient Boosting, LR: Logistic Regression, SVM: Support Vector Machines, RF: Random Forest, KNN: K-nearest Neighbor, LASSO: Least Absolute Shrinkage and Selection Operator)

From: Gradient boosting for Parkinson’s disease diagnosis from voice recordings

Metrics   Accuracy Metrics with 95% CI  
LGB XGB LR SVM RF KNN LASSO
F1 0.839 [0.831–0.847] 0.810 [0.802–0.819] 0.771 [0.762–0.780] 0.730 [0.721–0.739] 0.810 [0.800–0.819] 0.744 [0.735–0.753] 0.763 [0.755–0.7723]
AUC 0.898 [0.892–0.905] 0.891 [0.885–0.898] 0.839 [0.830–0.847 0.838 [0.830–0.846] 0.884 [0.876–0.892] 0.841 [0.834–0.848] 0.870 [0.863–0.877]
Accuracy 0.841 [0.833–0.849] 0.816 [0.809–0.823] 0.771 [0.762–0.780] 0.744 [0.735–0.752] 0.818 [0.810–0.826] 0.760 [0.752–0.768] 0.761 [0.753–0.769]
Sensitivity 0.839 [0.827–0.850] 0.801 [0.789–0.813] 0.777 [0.765–0.790] 0.704 [0.691–0.716] 0.795 [0.782–0.808] 0.712 [0.699–0.725] 0.782 [0.769–0.794]
Specificity 0.844 [0.832–0.855] 0.830 [0.819–0.841] 0.764 [0.750–0.778] 0.784 [0.771–0.798] 0.841 [0.831–0.852] 0.807 [0.796–0.818] 0.741 [0.729–0.754]
PPV 0.853 [0.843–0.863] 0.835 [0.825–0.845] 0.780 [0.769–0.791] 0.780 0.769–0.791] 0.844 [0.834–0.854] 0.796 [0.786–0.806] 0.762 [0.753–0.772]
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