Table 5.

TCM prescription and non-TCM recipe classification accuracies of the artificial intelligence classification systems, kNN and SVM, evaluated by threefold cross-validation study

Cross validation	Training set		Testing set
	TCM prescriptions	Non-TCM recipes	TP	FN	P+ %	TN	FP	P− %	P%
AI classification system: kNN
1	388	7453	341	47	87.9	3706	43	98.8	97.8
2	384	7468	314	70	81.8	3699	35	99.1	97.4
3	389	7483	325	64	83.6	3670	49	98.7	97.3
Average Accuracy					84.4			98.9	97.5
AI classification system: SVM
1	388	7453	360	28	92.8	3692	57	98.4	97.9
2	384	7468	342	42	89.1	3684	50	98.6	97.7
3	389	7483	358	31	92.0	3670	49	98.7	98.1
Average accuracy					91.3			98.6	97.9

P+, P− and P represent the classification accuracy for TCM prescriptions, non-TCM recipes and all recipes, respectively. TP, TN, FP and FN are the number of true positive (correctly classified TCM prescriptions), true negative (correctly classified non-TCM recipes), false positive (TCM prescriptions falsely classified as non-TCM recipes) and false negative (non-TCM recipes falsely classified as TCM prescriptions), respectively, and N is the total number of recipes.

Bold numerals are used to highlight the average accuracies of the two AI systems.