Table 3.

Accuracy and AUC values of all models

		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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	AUC	0.8198	0.8597	0. 8636	0.7782	0.8194	0.8179	0.7064	0.8542	0.8322