
Diagnosis (model/clinical diagnosis)

χ^{2}

Kappa value

p value

AUC^{i}


+/+ ^{a}
 ∓^{b}

±^{c}

−/−^{d}


ResultSVM^{h}

237

43

46

262

0.1011

0.697

0.751

0.788

Sensitivity^{e} (%)

84.64
    
Specificity^{f} (%)

85.06
    
Accuracy^{g} (%)

84.86
    
ResultRF^{h}

231

49

38

270

1.3908

0.703

0.238

0.803

Sensitivity^{e} (%)

82.50
    
Specificity^{f} (%)

87.66
    
Accuracy^{g} (%)

85.20
    
ResultXGB^{h}

252

28

34

274

0.2903

0.789

0.446

0.830

Sensitivity^{e} (%)

90.00
    
Specificity^{f} (%)

88.96
    
Accuracy^{g} (%)

89.46
    
 SVM: support vector machine; RF: random forest; XGB: XGBoot; FN: false negative; FP: false positive; AUC: area under curve
 ^{a}Our model or clinical diagnosis were both positivechildren were with leukemia
 ^{b}Our model diagnosed children as normal, but the clinical diagnosis of them was leukemia
 ^{c}Our model diagnosed children as leukemia, but the clinical diagnosis of them was normal
 ^{d}Our model or clinical diagnosis were both negative, and children were normal
 ^{e}Number of +/+ for each model/(number of +/+ for each model plus number of ∓ for each model) × 100%
 ^{f}Number of −/− for each model/(number of −/− for each model plus number of ± for each model) × 100%
 ^{g}(Number of −/− for each model plus number of +/+ for each model)/588 × 100%
 ^{h}McNemar’s test
 ^{i}ROC analysis