Table 2 Summary of cross-validated prediction models on trained data (n = 3473)
Model | AUC | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) |
|---|---|---|---|---|---|
Model 1 | |||||
Logistic regression | 0.73 [0.71–0.75] | 64 [60–68] | 70 [68–71] | 32 [30–35] | 89 [88–91] |
Elastic net | 0.74 [0.72–0.76] | 68 [64–71] | 69 [67–71] | 33 [31–36] | 90 [89–92] |
Random forest | 0.74 [0.72–0.76] | 6 [4–8] | 99 [99–99] | 60 [47–71] | 82 [81–83] |
SVM | 0.65 [0.62–0.68] | 49 [45–53] | 73 [71–74] | 29 [27–32] | 86 [85–88] |
Model 2 | |||||
Logistic regression | 0.74 [0.72–0.76] | 67 [63–70] | 70 [68–72] | 34 [31–36] | 90 [89–91] |
Elastic net | 0.74 [0.72–0.76] | 67 [63–70] | 69 [67–71] | 33 [30–36] | 90 [89–91] |
Random forest | 0.74 [0.72–0.76] | 47 [43–51] | 84 [82–85] | 40 [36–44] | 88 [86–89] |
SVM | 0.73 [0.71–0.75] | 72 [69–76] | 63 [61–65] | 31 [28–33] | 91 [89–92] |
Model 3 | |||||
Logistic regression | 0.71 [0.68–0.74] | 64 [60–68] | 68 [66–70] | 31 [29–34] | 89 [88–91] |
Elastic net | 0.71 [0.68–0.74] | 64 [60–67] | 67 [65–69] | 31 [28–33] | 89 [88–90] |
Random forest | 0.71 [0.68–0.74] | 55 [51–59] | 75 [73–77] | 34 [31–37] | 88 [87–89] |
SVM | 0.70 [0.67–0.73] | 69 [65–73] | 61 [59–63] | 29 [27–31] | 90 [88–91] |