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Table 4 The validation of models on AL diagnosis for Out-Sample Test

From: Mathematical models of amino acid panel for assisting diagnosis of children acute leukemia

  Diagnosis (model/clinical diagnosis) χ2 Kappa value p value AUCi
+/+ a b ±c −/−d
Result-SVMh 237 43 46 262 0.1011 0.697 0.751 0.788
Sensitivitye (%) 84.64     
Specificityf (%) 85.06     
Accuracyg (%) 84.86     
Result-RFh 231 49 38 270 1.3908 0.703 0.238 0.803
Sensitivitye (%) 82.50     
Specificityf (%) 87.66     
Accuracyg (%) 85.20     
Result-XGBh 252 28 34 274 0.2903 0.789 0.446 0.830
Sensitivitye (%) 90.00     
Specificityf (%) 88.96     
Accuracyg (%) 89.46     
  1. SVM: support vector machine; RF: random forest; XGB: XGBoot; FN: false negative; FP: false positive; AUC: area under curve
  2. aOur model or clinical diagnosis were both positive-children were with leukemia
  3. bOur model diagnosed children as normal, but the clinical diagnosis of them was leukemia
  4. cOur model diagnosed children as leukemia, but the clinical diagnosis of them was normal
  5. dOur model or clinical diagnosis were both negative, and children were normal
  6. eNumber of +/+ for each model/(number of +/+ for each model plus number of for each model) × 100%
  7. fNumber of −/− for each model/(number of −/− for each model plus number of ± for each model) × 100%
  8. g(Number of −/− for each model plus number of +/+ for each model)/588 × 100%
  9. hMcNemar’s test
  10. iROC analysis