Data
colic

colic

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Author: Mary McLeish & Matt Cecile, University of Guelph Donor: Will Taylor (taylor@pluto.arc.nasa.gov) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Horse+Colic) - 8/6/89 Horse Colic database Database of surgeries on horses. Possible class attributes: 24 (whether lesion is surgical), others include: 23, 25, 26, and 27 Notes: * Hospital_Number is an identifier and should be ignored when modelling Attribute Information: > 1: surgery? 1 = Yes, it had surgery 2 = It was treated without surgery 2: Age 1 = Adult horse 2 = Young (< 6 months) 3: Hospital Number - numeric id - the case number assigned to the horse (may not be unique if the horse is treated > 1 time) 4: rectal temperature - linear - in degrees celsius. - An elevated temp may occur due to infection. - temperature may be reduced when the animal is in late shock - normal temp is 37.8 - this parameter will usually change as the problem progresses eg. may start out normal, then become elevated because of the lesion, passing back through the normal range as the horse goes into shock 5: pulse - linear - the heart rate in beats per minute - is a reflection of the heart condition: 30 -40 is normal for adults - rare to have a lower than normal rate although athletic horses may have a rate of 20-25 - animals with painful lesions or suffering from circulatory shock may have an elevated heart rate 6: respiratory rate - linear - normal rate is 8 to 10 - usefulness is doubtful due to the great fluctuations 7: temperature of extremities - a subjective indication of peripheral circulation - possible values: 1 = Normal 2 = Warm 3 = Cool 4 = Cold - cool to cold extremities indicate possible shock - hot extremities should correlate with an elevated rectal temp. 8: peripheral pulse - subjective - possible values are: 1 = normal 2 = increased 3 = reduced 4 = absent - normal or increased p.p. are indicative of adequate circulation while reduced or absent indicate poor perfusion 9: mucous membranes - a subjective measurement of colour - possible values are: 1 = normal pink 2 = bright pink 3 = pale pink 4 = pale cyanotic 5 = bright red / injected 6 = dark cyanotic - 1 and 2 probably indicate a normal or slightly increased circulation - 3 may occur in early shock - 4 and 6 are indicative of serious circulatory compromise - 5 is more indicative of a septicemia 10: capillary refill time - a clinical judgement. The longer the refill, the poorer the circulation - possible values 1 = < 3 seconds 2 = >= 3 seconds 11: pain - a subjective judgement of the horse's pain level - possible values: 1 = alert, no pain 2 = depressed 3 = intermittent mild pain 4 = intermittent severe pain 5 = continuous severe pain - should NOT be treated as a ordered or discrete variable! - In general, the more painful, the more likely it is to require surgery - prior treatment of pain may mask the pain level to some extent 12: peristalsis - an indication of the activity in the horse's gut. As the gut becomes more distended or the horse becomes more toxic, the activity decreases - possible values: 1 = hypermotile 2 = normal 3 = hypomotile 4 = absent 13: abdominal distension - An IMPORTANT parameter. - possible values 1 = none 2 = slight 3 = moderate 4 = severe - an animal with abdominal distension is likely to be painful and have reduced gut motility. - a horse with severe abdominal distension is likely to require surgery just tio relieve the pressure 14: nasogastric tube - this refers to any gas coming out of the tube - possible values: 1 = none 2 = slight 3 = significant - a large gas cap in the stomach is likely to give the horse discomfort 15: nasogastric reflux - possible values 1 = none 2 = > 1 liter 3 = < 1 liter - the greater amount of reflux, the more likelihood that there is some serious obstruction to the fluid passage from the rest of the intestine 16: nasogastric reflux PH - linear - scale is from 0 to 14 with 7 being neutral - normal values are in the 3 to 4 range 17: rectal examination - feces - possible values 1 = normal 2 = increased 3 = decreased 4 = absent - absent feces probably indicates an obstruction 18: abdomen - possible values 1 = normal 2 = other 3 = firm feces in the large intestine 4 = distended small intestine 5 = distended large intestine - 3 is probably an obstruction caused by a mechanical impaction and is normally treated medically - 4 and 5 indicate a surgical lesion 19: packed cell volume - linear - the # of red cells by volume in the blood - normal range is 30 to 50. The level rises as the circulation becomes compromised or as the animal becomes dehydrated. 20: total protein - linear - normal values lie in the 6-7.5 (gms/dL) range - the higher the value the greater the dehydration 21: abdominocentesis appearance - a needle is put in the horse's abdomen and fluid is obtained from the abdominal cavity - possible values: 1 = clear 2 = cloudy 3 = serosanguinous - normal fluid is clear while cloudy or serosanguinous indicates a compromised gut 22: abdomcentesis total protein - linear - the higher the level of protein the more likely it is to have a compromised gut. Values are in gms/dL 23: outcome - what eventually happened to the horse? - possible values: 1 = lived 2 = died 3 = was euthanized 24: surgical lesion? - retrospectively, was the problem (lesion) surgical? - all cases are either operated upon or autopsied so that this value and the lesion type are always known - possible values: 1 = Yes 2 = No 25, 26, 27: type of lesion - first number is site of lesion 1 = gastric 2 = sm intestine 3 = lg colon 4 = lg colon and cecum 5 = cecum 6 = transverse colon 7 = retum/descending colon 8 = uterus 9 = bladder 11 = all intestinal sites 00 = none - second number is type 1 = simple 2 = strangulation 3 = inflammation 4 = other - third number is subtype 1 = mechanical 2 = paralytic 0 = n/a - fourth number is specific code 1 = obturation 2 = intrinsic 3 = extrinsic 4 = adynamic 5 = volvulus/torsion 6 = intussuption 7 = thromboembolic 8 = hernia 9 = lipoma/slenic incarceration 10 = displacement 0 = n/a 28: cp_data - is pathology data present for this case? 1 = Yes 2 = No - this variable is of no significance since pathology data is not included or collected for these cases

28 features

surgical_lesion (target)nominal2 unique values
0 missing
surgerynominal2 unique values
2 missing
Agenominal2 unique values
0 missing
Hospital_Number (ignore)nominal337 unique values
0 missing
rectal_temperaturenumeric40 unique values
69 missing
pulsenumeric54 unique values
26 missing
respiratory_ratenumeric40 unique values
71 missing
temperature_of_extremitiesnominal4 unique values
65 missing
peripheral_pulsenominal4 unique values
83 missing
mucous_membranesnominal6 unique values
48 missing
capillary_refill_timenominal3 unique values
38 missing
painnominal5 unique values
63 missing
peristalsisnominal4 unique values
52 missing
abdominal_distensionnominal4 unique values
65 missing
nasogastric_tubenominal3 unique values
131 missing
nasogastric_refluxnominal3 unique values
133 missing
nasogastric_reflux_PHnumeric24 unique values
299 missing
rectal_examination_-_fecesnominal4 unique values
128 missing
abdomennominal5 unique values
143 missing
packed_cell_volumenumeric54 unique values
37 missing
total_proteinnumeric84 unique values
43 missing
abdominocentesis_appearancenominal3 unique values
194 missing
abdomcentesis_total_proteinnumeric44 unique values
235 missing
outcomenominal3 unique values
2 missing
site_of_lesionnominal63 unique values
0 missing
type_of_lesionnominal8 unique values
0 missing
subtype_of_lesionnominal2 unique values
0 missing
pathology_cp_datanominal2 unique values
0 missing

107 properties

368
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
1927
Number of missing values in the dataset.
361
Number of instances with at least one value missing.
7
Number of numeric attributes.
21
Number of nominal attributes.
13.26
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
5
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
22.33
Average number of distinct values among the attributes of the nominal type.
0.02
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
73.27
Standard deviation of the number of distinct values among attributes of the nominal type.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.71
Mean skewness among attributes of the numeric type.
1.93
First quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.18
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
12.71
Mean standard deviation of attributes of the numeric type.
1.38
Second quartile (Median) of entropy among attributes.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
63.04
Percentage of instances belonging to the most frequent class.
0.03
Minimal entropy among attributes.
0.84
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
Entropy of the target attribute values.
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
232
Number of instances belonging to the most frequent class.
-0.93
Minimum kurtosis among attributes of the numeric type.
30.52
Second quartile (Median) of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
8.35
Maximum entropy among attributes.
2.95
Minimum of means among attributes of the numeric type.
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.18
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
2.65
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.96
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
70.76
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
17.86
Percentage of binary attributes.
10.87
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.08
Number of attributes divided by the number of instances.
0.92
Maximum mutual information between the nominal attributes and the target attribute.
-0.46
Minimum skewness among attributes of the numeric type.
98.1
Percentage of instances having missing values.
1.91
Third quartile of entropy among attributes.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
7.47
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
337
The maximum number of distinct values among attributes of the nominal type.
0.71
Minimum standard deviation of attributes of the numeric type.
18.7
Percentage of missing values.
2.03
Third quartile of kurtosis among attributes of the numeric type.
0.54
Average class difference between consecutive instances.
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.58
Maximum skewness among attributes of the numeric type.
36.96
Percentage of instances belonging to the least frequent class.
25
Percentage of numeric attributes.
45.66
Third quartile of means among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
28.09
Maximum standard deviation of attributes of the numeric type.
136
Number of instances belonging to the least frequent class.
75
Percentage of nominal attributes.
0.1
Third quartile of mutual information between the nominal attributes and the target attribute.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.81
Average entropy of the attributes.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.98
First quartile of entropy among attributes.
1.39
Third quartile of skewness among attributes of the numeric type.
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.89
Mean kurtosis among attributes of the numeric type.
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.93
First quartile of kurtosis among attributes of the numeric type.
27.7
Third quartile of standard deviation of attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
31.11
Mean of means among attributes of the numeric type.
0.13
Average mutual information between the nominal attributes and the target attribute.
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.96
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

13 tasks

122 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: pathology_cp_data
86 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: surgical_lesion
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: pathology_cp_data
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: surgical_lesion
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: pathology_cp_data
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: pathology_cp_data
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: pathology_cp_data
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: pathology_cp_data
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: pathology_cp_data
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: pathology_cp_data
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: surgical_lesion
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