Data-driven meal events detection using blood glucose response patterns

Table 3 Model Selection metrics for all participants. Resulting values of the model selection metrics of the models validated on the data of each of the participants

ID	Classifier	Meals	Predictions	FP	FN	TP	PPV	TPR	F\(_1\)-score	F\(_2\)-score
540	Decision Tree	23	9	4	18	5	0.56	0.22	0.31	0.25
544	Random Forest	31	43	15	3	28	0.65	0.90	0.76	0.84
552	MLP	3	14	13	2	1	0.07	0.33	0.12	0.19
559	Gaussian NB	25	32	18	11	14	0.44	0.56	0.49	0.53
563	Decision Tree	25	34	19	10	15	0.44	0.60	0.51	0.56
567	Gradient Boosting	1	6	5	0	1	0.17	1.00	0.29	0.50
570	Random Forest	29	40	19	8	21	0.52	0.72	0.61	0.67
575	MLP	46	53	17	10	36	0.68	0.78	0.73	0.76
584	Decision Tree	15	20	11	6	9	0.45	0.60	0.51	0.56
588	AdaBoost	43	49	17	11	32	0.65	0.74	0.70	0.72
591	AdaBoost	40	65	35	10	30	0.46	0.75	0.57	0.67
596	Gradient Boosting	44	54	25	15	29	0.54	0.66	0.59	0.63

FP false positives, FN false negatives, TP true positives, PPV (precision) positive predictive value, TPR (recall) true positive rate

ISSN: 1472-6947