Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder

Huang, Yuanyuan; Yi, Yun; Chen, Qiang; Li, Hehua; Feng, Shixuan; Zhou, Sumiao; Zhang, Ziyun; Liu, Chenyu; Li, Junhao; Lu, Qiuling; Zhang, Lida; Han, Wei; Wu, Fengchun; Ning, Yuping

doi:10.1186/s12888-023-05349-9

Table 2 The classification results of different sets

From: Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder

Feature selection	Model	Accuracy	Precision	Recall	AUC	p-value
Set1
SVM-RFE	DT	0.751 ± 0.017	0.774 ± 0.016	0.773 ± 0.013	0.749 ± 0.017	0.0099
	SVM	0.776 ± 0.010	0.787 ± 0.013	0.809 ± 0.012	0.774 ± 0.010	0.0099
	GBDT	0.803 ± 0.013	0.817 ± 0.014	0.817 ± 0.013	0.800 ± 0.012	0.0099
	NB	0.679 ± 0.014	0.741 ± 0.019	0.627 ± 0.013	0.681 ± 0.014	0.0099
	KNN	0.772 ± 0.014	0.731 ± 0.014	0.913 ± 0.012	0.755 ± 0.015	0.0099
LASSO-LR	DT	0.830 ± 0.012	0.843 ± 0.013	0.846 ± 0.013	0.828 ± 0.012	0.0099
	SVM	0.859 ± 0.012	0.896 ± 0.011	0.837 ± 0.022	0.861 ± 0.010	0.0099
	GBDT	0.826 ± 0.008	0.845 ± 0.013	0.831 ± 0.013	0.827 ± 0.008	0.0099
	NB	0.661 ± 0.013	0.778 ± 0.018	0.525 ± 0.016	0.675 ± 0.012	0.0099
	KNN	0.851 ± 0.012	0.811 ± 0.013	0.946 ± 0.010	0.842 ± 0.013	0.0099
PCA	DT	0.792 ± 0.014	0.808 ± 0.015	0.809 ± 0.020	0.791 ± 0.015	0.0099
	SVM	0.870 ± 0.009	0.891 ± 0.011	0.867 ± 0.015	0.871 ± 0.010	0.0099
	GBDT	0.813 ± 0.013	0.832 ± 0.017	0.823 ± 0.015	0.813 ± 0.013	0.0099
	NB	0.687 ± 0.013	0.768 ± 0.013	0.608 ± 0.027	0.694 ± 0.013	0.0099
	KNN	0.882 ± 0.006	0.847 ± 0.008	0.955 ± 0.011	0.877 ± 0.008	0.0099
Set2
SVM-RFE	DT	0.543 ± 0.034	0.596 ± 0.034	0.577 ± 0.047	0.541 ± 0.035	0.0099
	SVM	0.590 ± 0.037	0.597 ± 0.022	0.845 ± 0.042	0.560 ± 0.037	0.0099
	GBDT	0.598 ± 0.027	0.615 ± 0.019	0.764 ± 0.035	0.580 ± 0.025	0.0099
	NB	0.513 ± 0.029	0.609 ± 0.041	0.353 ± 0.064	0.532 ± 0.026	0.0099
	KNN	0.561 ± 0.017	0.576 ± 0.012	0.817 ± 0.033	0.527 ± 0.019	0.0099
LASSO-LR	DT	0.553 ± 0.024	0.599 ± 0.026	0.602 ± 0.033	0.544 ± 0.027	0.0099
	SVM	0.618 ± 0.032	0.622 ± 0.021	0.822 ± 0.028	0.594 ± 0.033	0.0099
	GBDT	0.585 ± 0.020	0.606 ± 0.013	0.748 ± 0.044	0.579 ± 0.046	0.0099
	NB	0.503 ± 0.026	0.607 ± 0.028	0.296 ± 0.073	0.524 ± 0.021	0.0099
	KNN	0.564 ± 0.019	0.573 ± 0.013	0.854 ± 0.036	0.521 ± 0.022	0.0099
PCA	DT	0.536 ± 0.030	0.585 ± 0.028	0.576 ± 0.049	0.524 ± 0.032	0.0099
	SVM	0.617 ± 0.039	0.615 ± 0.028	0.837 ± 0.025	0.579 ± 0.042	0.0099
	GBDT	0.575 ± 0.032	0.601 ± 0.019	0.730 ± 0.035	0.560 ± 0.027	0.0099
	NB	0.582 ± 0.020	0.623 ± 0.018	0.650 ± 0.048	0.575 ± 0.021	0.0099
	KNN	0.553 ± 0.025	0.571 ± 0.018	0.825 ± 0.039	0.517 ± 0.028	0.0099
Set3
SVM-RFE	DT	0.721 ± 0.015	0.787 ± 0.019	0.723 ± 0.020	0.719 ± 0.011	0.0099
	SVM	0.755 ± 0.012	0.753 ± 0.010	0.768 ± 0.005	0.719 ± 0.012	0.0099
	GBDT	0.786 ± 0.011	0.768 ± 0.12	0.808 ± 0.011	0.778 ± 0.014	0.0099
	NB	0.639 ± 0.013	0.732 ± 0.021	0.590 ± 0.012	0.655 ± 0.016	0.0099
	KNN	0.758 ± 0.012	0.708 ± 0.019	0.865 ± 0.020	0.734 ± 0.017	0.0099
LASSO_LR	DT	0.828 ± 0.011	0.835 ± 0.018	0.833 ± 0.012	0.802 ± 0.012	0.0099
	SVM	0.837 ± 0.013	0.876 ± 0.008	0.822 ± 0.003	0.842 ± 0.015	0.0099
	GBDT	0.811 ± 0.012	0.832 ± 0.011	0.822 ± 0.022	0.838 ± 0.022	0.0099
	NB	0.661 ± 0.009	0.789 ± 0.012	0.588 ± 0.011	0.655 ± 0.008	0.0099
	KNN	0.844 ± 0.013	0.798 ± 0.018	0.900 ± 0.011	0.802 ± 0.021	0.0099
PCA	DT	0.786 ± 0.016	0.819 ± 0.012	0.758 ± 0.019	0.788 ± 0.016	0.0099
	SVM	0.845 ± 0.011	0.887 ± 0.014	0.856 ± 0.011	0.886 ± 0.012	0.0099
	GBDT	0.822 ± 0.012	0.844 ± 0.017	0.818 ± 0.011	0.823 ± 0.019	0.0099
	NB	0.667 ± 0.011	0.768 ± 0.023	0.668 ± 0.015	0.688 ± 0.011	0.0099
	KNN	0.876 ± 0.018	0.833 ± 0.012	0.923 ± 0.015	0.857 ± 0.012	0.0099

AUC: Area Under the Cure; p-value: Permutation test p value; SVM-RFE: Support Vector Machines- Recursive Feature Elimination; LASSO_LR: Least Absolute Shrinkage and Selection Operator- Logistic Regression; PCA: Principal Component Analysis; DT: Decision Tree; GBDT: Gradient Boosting Decision Tree; NB: Naïve Bayesian; KNN: K-Nearest Neighbor

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