[Lactato sanguíneo e peritoneal na cólica equina: aplicação no atendimento de emergência e construção de uma árvore de decisão]

Figure 1
Decision tree constructed using the REPTree algorithm. After data mining from the data collected in the medical records, the respective algorithm was able to identify the parameters related to the patient's death or discharge. It is worth remembering that the parameters collected were heart rate, blood lactate (BL), peritoneal lactate (PL) and BL:PL ratio.

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Figure 2
Decision tree constructed using the J48 algorithm. After data mining from the data collected in the medical records, the respective algorithm was able to identify the parameters related to the patient's death or discharge. It is worth remembering that the parameters collected were heart rate, blood lactate (BL), peritoneal lactate (PL) and BL:PL ratio.

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Table 1
Diagnoses assigned to horses that died or survived the first 24 hours of hospitalization (non-survivors and survivors respectively). Part 1

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Table 1
Diagnoses assigned to horses that died or survived the first 24 hours of hospitalization (non-survivors and survivors respectively). Part 2

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Table 2
Variables of interest measured in horses that died or survived the first 24 hours of Hospitalization (non-survivors and survivors respectively). Correlation coefficients less than 0.3, between 0.3 and 0.6 and greater than 0.6 indicated weak, moderate and strong correlation respectively

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Table 3
Correlations between selected clinical variables in horses that survived the first 24 hours of hospitalization. . Variables of interest measured in horses that died or survived the first 24 hours of Hospitalization (non-survivors and survivors respectively). Correlation coefficients less than 0.3, between 0.3 and 0.6 and greater than 0.6 indicated weak, moderate and strong correlation respectively

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Table 4
Correlations between selected clinical variables in horses that did not survive the first 24 hours of hospitalization. . Variables of interest measured in horses that died or survived the first 24 hours of Hospitalization (non-survivors and survivors respectively). Correlation coefficients less than 0.3, between 0.3 and 0.6 and greater than 0.6 indicated weak, moderate, and strong correlation respectively

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Table 5
Performance analysis of data mining algorithms. J48, Random Forest and REPTree perform better

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Table 6
Metrics for evaluating data mining algorithms according to the occurrence of survival or death. Attention to AUROC (comparison between predicted and actual outcomes to assess the discriminating ability of the prognostic model). Note that J48 and REPTree Algorithms used to construct the decision trees in this work present AROUC indicative of good performance

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Table 7
Metrics for evaluating data mining algorithms. Pay attention to the percentage of instances classified as correctly or incorrectly by the J48 and REPTree algorithms

Condition	Non-survivors	Survivors
Incarcerated inguinal hernia	1	5
Post-ovulation hemoperitoneum	-	1
Mesodiverticular band entrapment	1	-
Atropine poisoning	-	1
Amitraz poisoning	-	1
Jejunal abscess	-	1
Pelvic flexure adhesion	-	1
Mesenteric root abscess	1	-
Sand impaction	-	1
Large colon or cecal tympanism	-	2
Large colon torsion	-	2
Colitis	-	3
Enterolithiasis	1	8
Gastric dilatation	-	5
Gastritis/stomach ulcer	-	7
Proximal enteritis	-	7
Nephrosplenic entrapment	-	5
Idiopathic episode of colic	-	8
Mesenteric tear with small colon entrapment	1	-
Mesenteric volvulus	1	-

Condition	Non-survivors	Survivors
Pedunculated lipoma	1	-
Liver lobe torsion	1	-
Intussusception	2	2
Small intestinal impaction	2	10
Large colon displacement	2	46
Enterocolitis	2	-
Small intestinal volvulus	2	1
Epiploic foramen entrapment	3	1
Mesenteric tear with small bowel entrapment	3	2
Large colon impaction	4	35
Large colon volvulus	4	-
Peritonitis	4	2
Intestinal or gastric rupture	5	-

Variable	Survivors	Non-survivors	P-value
Blood lactate (mmol/L)	4.07 ± 2.88	6.78 ± 4.89	0.0089
Peritoneal lactate (mmol/L)	5.31 ± 2.88	11.44 ± 6.93	<.0001
BL:PL	1.03 ± 0.55	1.11 ± 1.26	0.3404
Dehydration (%)	4.95 ± 1.91	7.02 ± 1.70	<.0001
Heart rate	61.56 ± 18.58	77.31 ± 23.42	<.0001

Variable	Blood lactate	Peritoneal lactate	BL:PL	Dehydration	Heart rate
Blood lactate	-	r = 0.69 p <.0001	r = 0.02 p = 0,8571	r = 0.20 p = 0.067	r = 0.36 p= 0.0012
Peritoneal lactate	-	-	r = -0.69 p <.0001	r = 0.37 p <.0001	r = 0.40 p < .0001
BL:PL	-	-	-	r = -0.60 p < .0001	r = 0.44 p < 0.0020
Dehydration	-	-	-	-	R = 0.36 p < .0001

Variable	Blood lactate	Peritoneal lactate	BL:PL	Dehydration	Heart rate
Blood lactate	-	r = 0.29 p = 0.2534	r = 0.25 p = 0.2534	r = 0.35 p = 0.1378	r = 0.01 p = 0.9532
Peritoneal lactate	-	-	r = -0.71 p = 0.0012	r = 0.55 p = 0.0010	r = 0.16 p = 0.34
BL:PL	-	-	-	r = -0.08 p = 0.7341	r = 0.31 p = 0.2457
Dehydration	-	-	-	-	r = 0.16 p = 0.3752

J48	Hoeffding Tree	Random Forest	Random Tree	REPTree
83.16	79.73	83.76	76.25	83.39
(v/ /*)	(0/1/0)	(0/1/0)	(0/1/0)	(0/1/0)

Algorithm	Class	TP Rate	FP Rate	Precision	Recall	F-Measure	AUROC
J48	Death	0.257	0.032	0.643	0.257	0.367	0.760
J48	Survival	0.968	0.743	0.851	0.968	0.906	0.706
Hoeffding Tree	Death	0.257	0.071	0.450	0.257	0.327	0.665
Hoeffding Tree	Survival	0.929	0.743	0.846	0.929	0.885	0.630
Random Forest	Death	0.286	0.039	0.625	0.286	0.392	0.789
Random Forest	Survival	0.961	0.714	0.855	0.961	0.905	0.713
Random Tree	Death	0.314	0,104	0.407	0.314	0.355	0.680
Random Tree	Survival	0.896	0.686	0.852	0.896	0.873	0.641
REPTree	Death	0.286	0.039	0.625	0.286	0.392	0.714
REPTree	Survival	0.961	0.714	0.855	0.961	0.905	0.682

Algorithm	Correctly Classified Instances	Incorrectly Classified Instances	Kappa statistic	MAE	RMSE	RAE	RRSE
J48	158 (83.59%)	31 (16.40%)	0.2925	0.2355	0.3484	77.42 %	89.64 %
Hoeffding Tree	152 (80.42 %)	37 (19.57%)	0.2226	0.2741	0.4046	90.11 %	104.10 %
Random Forest	158 (83.59 %)	31 (16.40 %)	0.3122	0.2158	0.3488	70.93 %	89.74 %
Random Tree	149 (78.83%)	40 (21.16 %)	0.2308	0.2329	0.4224	76.56 %	108.68 %
REPTree	158 (83.59 %)	31 (16.40 %)	0.3122	0.2516	0.3636	82.69 %	93.55 %

[1] Corresponding author: alinebarros219@gmail.com

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[Lactato sanguíneo e peritoneal na cólica equina: aplicação no atendimento de emergência e construção de uma árvore de decisão]

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