Table 3.

Performance of XGBoost machine learning classification with hyperparameter optimization in distinguishing between non-CKD and CKD cortical bone from HRpQCT images using the test dataset.

			Classification Performance Metrics
Model	Location	Group	Precision	Recall	F1 score	Accuracy	AUC-ROC
Model 1	Radius Distal	Non-CKD	0.97	0.96	0.97	0.96	0.98
		CKD	0.93	0.94	0.94	0.94
Model 2	Radius Diaphyseal	Non-CKD	0.99	0.98	0.99	0.99	0.99
		CKD	0.98	0.98	0.98	0.98
Model 3	Tibia Distal	Non-CKD	0.96	0.98	0.97	0.95	0.98
		CKD	0.96	0.92	0.94	0.92
Model 4	Tibia Diaphyseal	Non-CKD	0.99	0.99	0.99	0.98	0.99
		CKD	0.99	0.99	0.99	0.99

Note: XGBoost is a gradient-boosted machine-learning decision tree or classifier. A probability threshold 0.5 was used to evaluate the precision, recall, F1 score, accuracy, AUC-ROC. The highest values in each column are bolded. CKD = chronic kidney disease; DSC = dice coefficient similarity; AUC-ROC = area under the receiver operating characteristic curve; XGBoost = Extreme Gradient Boosting.