Table 5 The performance metrics of traditional classification models.

From: Classification of musculoskeletal pain using machine learning

Pain type	Model	Accuracy	Balanced accuracy	ROC AUC	F1 score	Precision	Recall	Time taken (s)
Neck pain	ExtraTreesClassifier	0.885	0.892	0.892	0.887	0.89	0.88	0.115
	BaggingClassifier	0.865	0.892	0.892	0.867	0.87	0.86	0.045
	RandomForestClassifier	0.854	0.833	0.833	0.852	0.85	0.85	0.169
	XGBClassifier	0.823	0.819	0.819	0.824	0.82	0.82	0.067
	ExtraTreeClassifier	0.823	0.819	0.819	0.824	0.82	0.82	0.015
Shoulder pain	RandomForestClassifier	0.927	0.922	0.922	0.927	0.93	0.92	0.190
	LGBMClassifier	0.927	0.919	0.919	0.926	0.93	0.92	0.055
	ExtraTreesClassifier	0.917	0.913	0.913	0.916	0.92	0.91	0.115
	ExtraTreeClassifier	0.896	0.886	0.886	0.895	0.89	0.89	0.015
	XGBClassifier	0.885	0.888	0.888	0.886	0.89	0.88	0.070
Elbow pain	ExtraTreesClassifier	0.896	0.798	0.798	0.888	0.89	0.88	0.115
	QuadraticDiscriminantAnalysis	0.896	0.783	0.783	0.885	0.88	0.88	0.015
	CalibratedClassifierCV	0.875	0.769	0.769	0.865	0.87	0.86	0.030
	SVC	0.875	0.769	0.769	0.865	0.87	0.86	0.015
	RandomForestClassifier	0.875	0.769	0.769	0.865	0.87	0.86	0.185
Wrist pain	XGBClassifier	0.938	0.924	0.924	0.937	0.94	0.93	0.060
	ExtraTreeClassifier	0.938	0.916	0.916	0.936	0.94	0.93	0.010
	RandomForestClassifier	0.938	0.909	0.909	0.936	0.94	0.93	0.165
	LGBMClassifier	0.927	0.901	0.901	0.925	0.93	0.92	0.045
	ExtraTreesClassifier	0.927	0.901	0.901	0.925	0.93	0.92	0.120
Thoracic spine pain	ExtraTreesClassifier	0.885	0.877	0.877	0.885	0.89	0.88	0.115
	SVC	0.875	0.854	0.854	0.871	0.87	0.87	0.017
	NuSVC	0.865	0.841	0.841	0.860	0.86	0.86	0.015
	RandomForestClassifier	0.854	0.839	0.839	0.852	0.85	0.85	0.162
	DecisionTreeClassifier	0.833	0.832	0.832	0.834	0.83	0.83	0.010
Low back pain	ExtraTreesClassifier	0.938	0.943	0.943	0.938	0.94	0.93	0.115
	RandomForestClassifier	0.896	0.889	0.889	0.895	0.9	0.89	0.185
	BaggingClassifier	0.875	0.889	0.889	0.876	0.88	0.87	0.045
	XGBClassifier	0.875	0.882	0.882	0.876	0.88	0.87	0.055
	LogisticRegression	0.875	0.882	0.882	0.876	0.88	0.87	0.020
Hip pain	ExtraTreesClassifier	0.958	0.882	0.882	0.956	0.96	0.95	0.110
	RandomForestClassifier	0.958	0.882	0.882	0.956	0.96	0.95	0.164
	XGBClassifier	0.948	0.876	0.876	0.946	0.95	0.94	0.055
	BaggingClassifier	0.948	0.853	0.853	0.944	0.95	0.94	0.040
	AdaBoostClassifier	0.938	0.847	0.847	0.934	0.94	0.93	0.125
Knee pain	ExtraTreesClassifier	0.906	0.881	0.881	0.904	0.91	0.9	0.125
	RandomForestClassifier	0.906	0.875	0.875	0.903	0.91	0.9	0.170
	BaggingClassifier	0.896	0.861	0.861	0.891	0.9	0.89	0.040
	LGBMClassifier	0.875	0.844	0.844	0.871	0.88	0.87	0.040
	XGBClassifier	0.865	0.836	0.836	0.861	0.87	0.86	0.055
Ankle pain	ExtraTreesClassifier	0.969	0.932	0.932	0.968	0.97	0.96	0.120
	LGBMClassifier	0.938	0.864	0.864	0.934	0.94	0.93	0.045
	DecisionTreeClassifier	0.927	0.857	0.857	0.924	0.93	0.92	0.010
	XGBClassifier	0.927	0.857	0.857	0.924	0.93	0.92	0.050
	RandomForestClassifier	0.917	0.818	0.818	0.910	0.92	0.91	0.165

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Table 5 The performance metrics of traditional classification models.

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