Predicting three-year risk of incident colorectal precancerous conditions using non-invasive predictors and machine-learning algorithms: A multi-center retrospective study

Table 4 Performance of different models for predicting advanced adenoma

Model	Area under the curve (95%CI)	Threshold	Sensitivity	Specificity
Training
XGBOOST (probability)	0.988 (0.980-0.996)	≥ 50%	96.23%	99.86%
LASSO (probability)	0.682 (0.596-0.768)	≥ 7.43%	50.94%	84.77%
Decision tree (1 vs 0)	0.667 (0.602-0.733)	≥ 1	35.85%	97.60%
NNET (1 vs 0)	0.575 (0.527-0.624)	≥ 1	9.43%	99.58%
SVM (1 vs 0)	0.519 (0.493-0.545)	≥ 1	3.77%	100.00%
Logistic model (probability)	0.691 (0.607-0.774)	≥ 8.87%	50.94%	85.47%
Internal validation
XGBOOST (probability)	0.825 (0.766-0.883)	≥ 50%	63.46%	99.86%
LASSO (probability)	0.616 (0.538-0.693)	≥ 7.43%	26.92%	83.36%
Decision tree (1 vs 0)	0.546 (0.502-0.590)	≥ 1	11.54%	97.60%
NNET (1 vs 0)	0.492 (0.488-0.497)	≥ 1	0.00%	98.45%
SVM (1 vs 0)	0.499 (0.498-0.501)	≥ 1	0.00%	99.86%
Logistic model (probability)	0.622 (0.545-0.698)	≥ 8.87%	26.92%	84.06%
External validation
XGBOOST (probability)	0.767 (0.642-0.892)	≥ 50%	61.54%	90.35%
LASSO (probability)	0.500 (0.500-0.500)	≥ 7.43%	-	-
Decision tree (1 vs 0)	0.561 (0.477-0.646)	≥ 1	23.08%	89.19%
NNET (1 vs 0)	0.552 (0.489-0.615)	≥ 1	11.54%	98.84%
SVM (1 vs 0)	0.500 (0.500-0.500)	≥ 1	-	-
Logistic model (probability)	0.706 (0.602-0.809)	≥ 8.87%	69.23%	67.95%

LASSO: Least absolute shrinkage and selection operator; NNET: Neural network; SVM: Support vector machine; XGBOOST: EXtreme Gradient Boosting.