Data

colic

active
ARFF
Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn

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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

surgical_lesion (target) | nominal | 2 unique values 0 missing | |

surgery | nominal | 2 unique values 2 missing | |

Age | nominal | 2 unique values 0 missing | |

Hospital_Number (ignore) | nominal | 337 unique values 0 missing | |

rectal_temperature | numeric | 40 unique values 69 missing | |

pulse | numeric | 54 unique values 26 missing | |

respiratory_rate | numeric | 40 unique values 71 missing | |

temperature_of_extremities | nominal | 4 unique values 65 missing | |

peripheral_pulse | nominal | 4 unique values 83 missing | |

mucous_membranes | nominal | 6 unique values 48 missing | |

capillary_refill_time | nominal | 3 unique values 38 missing | |

pain | nominal | 5 unique values 63 missing | |

peristalsis | nominal | 4 unique values 52 missing | |

abdominal_distension | nominal | 4 unique values 65 missing | |

nasogastric_tube | nominal | 3 unique values 131 missing | |

nasogastric_reflux | nominal | 3 unique values 133 missing | |

nasogastric_reflux_PH | numeric | 24 unique values 299 missing | |

rectal_examination_-_feces | nominal | 4 unique values 128 missing | |

abdomen | nominal | 5 unique values 143 missing | |

packed_cell_volume | numeric | 54 unique values 37 missing | |

total_protein | numeric | 84 unique values 43 missing | |

abdominocentesis_appearance | nominal | 3 unique values 194 missing | |

abdomcentesis_total_protein | numeric | 44 unique values 235 missing | |

outcome | nominal | 3 unique values 2 missing | |

site_of_lesion | nominal | 63 unique values 0 missing | |

type_of_lesion | nominal | 8 unique values 0 missing | |

subtype_of_lesion | nominal | 2 unique values 0 missing | |

pathology_cp_data | nominal | 2 unique values 0 missing |

0.18

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

0.05

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0.6

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.61

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

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.

0.8

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.56

Maximum mutual information between the nominal attributes and the target attribute.

2

The minimal number of distinct values among attributes of the nominal type.

10.87

Second quartile (Median) of standard deviation of attributes of the numeric type.

0.26

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

11.13

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

63

The maximum number of distinct values among attributes of the nominal type.

0.46

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

2.03

Third quartile of kurtosis among attributes of the numeric type.

0.8

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

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

0.26

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

0.09

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

0.46

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

0.87

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

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

0.8

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

-0.93

First quartile of kurtosis among attributes of the numeric type.

27.7

Third quartile of standard deviation of attributes of the numeric type.

0.18

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

0.26

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

0.09

Average mutual information between the nominal attributes and the target attribute.

0.87

Area Under the ROC Curve 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

0.46

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

16.21

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

0.02

First quartile of mutual information between the nominal attributes and the target attribute.

0.6

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

13.36

Standard deviation of the number of distinct values among attributes of the nominal type.

6.6

Average number of distinct values among the attributes of the nominal type.

0.02

First quartile of skewness among attributes of the numeric type.

0.87

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

1.93

First quartile of standard deviation of attributes of the numeric type.

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

0.6

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.84

Second quartile (Median) of kurtosis among attributes of the numeric type.

0.87

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

30.52

Second quartile (Median) of means among attributes of the numeric type.