Sch-net: a deep learning architecture for automatic detection of schizophrenia

Fu, Jia; Yang, Sen; He, Fei; He, Ling; Li, Yuanyuan; Zhang, Jing; Xiong, Xi

doi:10.1186/s12938-021-00915-2

BioMedical Engineering OnLine

Table 4 Performance of feature engineering and classifiers on schizophrenic speech detection

From: Sch-net: a deep learning architecture for automatic detection of schizophrenia

Classifier		Feature
		Time-domain feature			FFT-based spectral feature		Auditory-based spectral feature		Spectral envelope feature
		STE	Pitch	Fluency feature	LTAS	Spectrogram	MFCC	GTCC	LP	SWLP	XLP
RF	Accuracy	0.7686	0.5935	0.8213	0.6464	0.8972	0.8043	0.8791	0.9245	0.9377	0.9423
	Precision	0.6251	0.5847	0.8281	0.6052	0.8946	0.7818	0.8487	0.9055	0.9319	0.9282
	Recall	0.7126	0.5754	0.8103	0.5545	0.9103	0.8577	0.9289	0.9549	0.9466	0.9644
	F1-score	0.6306	0.6322	0.8133	0.5513	0.8972	0.8144	0.8856	0.9280	0.9391	0.9453
KNN	Accuracy	0.7723	0.5385	0.7390	0.7504	0.8974	0.8626	0.8753	0.9204	0.9287	0.9375
	Precision	0.6410	0.5308	0.7050	0.6489	0.8566	0.8977	0.9043	0.8939	0.9117	0.9196
	Recall	0.6063	0.5597	0.7985	0.6152	0.9636	0.8312	0.8494	0.9640	0.9549	0.9636
	F1-score	0.6123	0.5382	0.7418	0.6211	0.9046	0.8591	0.8700	0.9257	0.9315	0.9398
SVM	Accuracy	0.7905	0.5172	0.7746	0.7358	0.9024	0.8625	0.8929	0.9164	0.9291	0.9334
	Precision	0.6447	0.5087	0.7657	0.6556	0.8741	0.8555	0.8762	0.8980	0.9183	0.9126
	Recall	0.5999	0.4767	0.7875	0.5435	0.9549	0.8929	0.9198	0.9470	0.9466	0.9636
	F1-score	0.6155	0.4644	0.7627	0.5813	0.9091	0.8689	0.8960	0.9206	0.9317	0.9356
LDA	Accuracy	0.7858	0.5087	0.7452	0.7314	0.8385	0.8887	0.9198	0.9026	0.9069	0.9109
	Precision	0.6447	0.4625	0.7083	0.6622	0.8053	0.9394	0.9479	0.8963	0.9053	0.8868
	Recall	0.5898	0.5391	0.7522	0.5380	0.9095	0.8474	0.8933	0.9198	0.9111	0.9462
	F1-score	0.6093	0.4710	0.7104	0.5821	0.8498	0.8807	0.9161	0.9060	0.9079	0.9146

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