Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis

Table 3

The classification accuracy result with selected features under different thresholds of information gain on candidate region.

Classifier	Lesions and their symmetrical regions threshold				Normal and their symmetrical regions threshold			Lesions and normal regions threshold
	0.0	0.1	0.2	0.3	0.0	0.1	0.2	0.0	0.1	0.2	0.3	0.4	0.5
Multilayer perceptron	0.4902	0.4974	0.5694	0.7269	0.5061	0.4983	0.4434	0.5476	0.5242	0.5291	0.5292	0.5125	0.7185
Decision tree	0.5980	0.6138	0.6603	0.6655	0.5941	0.6333	0.5841	0.7155	0.7423	0.7782	0.7742	0.7982	0.8230
Random forest	0.5897	0.6452	0.7036	0.7206	0.6055	0.6782	0.5775	0.7700	0.7932	0.8401	0.8260	0.8162	0.8360
Adaboost	0.5850	0.6263	0.6811	0.6818	0.5946	0.6651	0.6001	0.7071	0.7276	0.7671	0.7862	0.7748	0.8291
Gradient boosting	0.5977	0.6346	0.6838	0.7463	0.5931	0.6505	0.5950	0.7517	0.7564	0.7903	0.7978	0.8017	0.8694
Bagging	0.6217	0.6567	0.6973	0.7249	0.6065	0.6529	0.5745	0.7530	0.7767	0.8169	0.8282	0.8144	0.8307
Bernoulli naive Bayes	0.5100	0.6164	0.6724	0.7105	0.4413	0.5175	0.4318	0.6557	0.6748	0.7253	0.7594	0.7566	0.6785
Gaussian naive Bayes	0.4743	0.6203	0.6661	0.6984	0.4801	0.4574	0.4439	0.3737	0.3935	0.8098	0.7842	0.8323	0.8605
Support vector machine	0.4184	0.4223	0.6903	0.4211	0.4382	0.4299	0.4326	0.6785	0.6785	0.6650	0.8123	0.7942	0.6785
-nearest neighbor	0.2686	0.4563	0.7188	0.7748	0.2690	0.3492	0.6137	0.5812	0.5585	0.6437	0.8153	0.8010	0.8673
Average		0.5789	0.6743	0.6870		0.5532	0.5296		0.6625	0.7365	0.7712	0.7701	0.7991
			0.6467			0.5414					0.7480