Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

Zhao, Yueyang; Fang, Li; Cui, Lei; Bai, Song

doi:10.1186/s12911-020-01180-4

BMC Medical Informatics and Decision Making

Table 3 Accuracy and AUC values of all models

From: Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

		hold out 80/20	hold out 70/30	hold out 60/40	CV 5 fold	CV 10 fold	CV 15 fold	bootstrap 50	bootstrap 100	bootstrap 200
Numerical IHD Dataset
Logistic regression	Accuracy	0.7018	0.7529	0.7368	0.7386	0.7278	0.7207	0.6223	0.7486	0.7470
Logistic regression	AUC	0.5374	0.6392	0.6105	0.6096	0.5951	0.5597	0.5388	0.6343	0.6102
Naive Bayes	Accuracy	0.7544	0.7412	0.7719	0.7245	0.7319	0.7312	0.6524	0.7705	0.7590
Naive Bayes	AUC	0.7392	0.7791	0.7999	0.6591	0.6740	0.7041	0.4966	0.6786	0.7719
CART	Accuracy	0.7719	0.7059	0.6930	0.7031	0.7036	0.7060	0.6567	0.7377	0.7349
CART	AUC	0.6827	0.3797	0.6999	0.6787	0.6097	0.6587	0.4495	0.6862	0.6694
C4.5	Accuracy	0.7193	0.7294	0.7544	0.7563	0.7284	0.7528	0.7167	0.7268	0.6747
C4.5	AUC	0.6520	0.6792	0.7569	0.6727	0.6991	0.7480	0.4422	0.6151	0.6233
C5.0	Accuracy	0.6667	0.6824	0.7544	0.7246	0.7318	0.7493	0.6652	0.7268	0.6747
C5.0	AUC	0.6478	0.7132	0.7716	0.6514	0.7150	0.7146	0.4847	0.3861	0.6498
C5.0 boosted	Accuracy	0.7018	0.7882	0.7544	0.7706	0.7499	0.7596	0.6695	0.7268	0.7590
C5.0 boosted	AUC	0.6420	0.7710	0.7686	0.7415	0.7130	0.7510	0.6600	0.6988	0.7849
Random Forest	Accuracy	0.7544	0.7765	0.8421	0.8023	0.8025	0.8123	0.7639	0.8033	0.7952
Random Forest	AUC	0.8181	0.8524	0.8630	0.7943	0.8268	0.8274	0.6923	0.8538	0.8533
Categrical IHD dataset
Logistic regression	Accuracy	0.6842	0.7411	0.7544	0.7563	0.7493	0.7483	0.5794	0.7377	0.7349
Logistic regression	AUC	0.5257	0.6448	0.6220	0.6255	0.6442	0.6322	0.5535	0.6191	0.6179
Naive Bayes	Accuracy	0.7544	0.7412	0.7632	0.7245	0.7319	0.7312	0.6481	0.7650	0.7590
Naive Bayes	AUC	0.7359	0.7812	0. 7986	0.6565	0.6745	0.7012	0.4976	0.6580	0.7711
CART	Accuracy	0.7368	0.7059	0.6930	0.7031	0.7108	0.7097	0.6567	0.7377	0.7349
CART	AUC	0.6653	0.3797	0. 6999	0.6787	0.5971	0.6575	0.4495	0.6862	0.6694
C4.5	Accuracy	0.7193	0.7412	0.7632	0.7456	0.7461	0.7774	0.7554	0.6831	0.7108
C4.5	AUC	0.4427	0.7037	0. 7580	0.6784	0.6818	0.7365	0.4641	0.5457	0.6575
C5.0	Accuracy	0.7193	0.6706	0.7544	0.7316	0.7459	0.7528	0.6395	0.7268	0.6747
C5.0	AUC	0.6171	0.6939	0. 7716	0.6775	0.6983	0.6994	0.6209	0.3861	0.6701
C5.0 boosted	Accuracy	0.7544	0.7529	0.7719	0.7598	0.7562	0.7943	0.6395	0.7541	0.7470
C5.0 boosted	AUC	0.7575	0.7283	0. 7084	0.7318	0.7335	0.7947	0.6209	0.7169	0.7596
Random Forest	Accuracy	0.7719	0.7765	0.8509	0.8093	0.8130	0.8123	0.7811	0.8197	0.7952
Random Forest	AUC	0.8198	0.8597	0. 8636	0.7782	0.8194	0.8179	0.7064	0.8542	0.8322

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