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echoMonths

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ARFF
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Survival treated as the class attribute
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
in Connectionist-Based Information Systems. Singapore: Springer-Verlag.
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1. Title: Echocardiogram Data
2. Source Information:
-- Donor: Steven Salzberg (salzberg@cs.jhu.edu)
-- Collector:
-- Dr. Evlin Kinney
-- The Reed Institute
-- P.O. Box 402603
-- Maimi, FL 33140-0603
-- Date Received: 28 February 1989
3. Past Usage:
-- 1. Salzberg, S. (1988). Exemplar-based learning: Theory and
implementation (Technical Report TR-10-88). Harvard University,
Center for Research in Computing Technology, Aiken Computation
Laboratory (33 Oxford Street; Cambridge, MA 02138).
-- Steve applied his EACH program to predict survival (i.e., life
or death), did not use the wall-motion attribute, and recorded 87
correct and 29 incorrect in an incremental application to this
database. He also showed that, by tuning EACH to this domain,
EACH was able to derive (non-incrementally) a set of 28
hyper-rectangles that could perfectly classify 119 instances.
-- 2. Kan, G., Visser, C., Kooler, J., & Dunning, A. (1986). Short
and long term predictive value of wall motion score in acute
myocardial infarction. British Heart Journal, 56, 422-427.
-- They predicted the same variable (whether patients will live
one year after a heart attack) using a different set of 345
instances. Their statistical test recorded a 61% accuracy
in predicting that a patient will die (post-hoc fit).
-- 3. Elvin Kinney (in communication with Steven Salzberg) reported
that a Cox regression application recorded a 60% accuracy
in predicting that a patient will die.
4. Relevant Information:
-- All the patients suffered heart attacks at some point in the past.
Some are still alive and some are not. The survival and still-alive
variables, when taken together, indicate whether a patient survived
for at least one year following the heart attack.
The problem addressed by past researchers was to predict from the
other variables whether or not the patient will survive at least
one year. The most difficult part of this problem is correctly
predicting that the patient will NOT survive. (Part of the difficulty
seems to be the size of the data set.)
5. Number of Instances: 132
6. Number of Attributes: 13 (all numeric-valued)
7. Attribute Information:
1. survival -- the number of months patient survived (has survived,
if patient is still alive). Because all the patients
had their heart attacks at different times, it is
possible that some patients have survived less than
one year but they are still alive. Check the second
variable to confirm this. Such patients cannot be
used for the prediction task mentioned above.
2. still-alive -- a binary variable. 0=dead at end of survival period,
1 means still alive
3. age-at-heart-attack -- age in years when heart attack occurred
4. pericardial-effusion -- binary. Pericardial effusion is fluid
around the heart. 0=no fluid, 1=fluid
5. fractional-shortening -- a measure of contracility around the heart
lower numbers are increasingly abnormal
6. epss -- E-point septal separation, another measure of contractility.
Larger numbers are increasingly abnormal.
7. lvdd -- left ventricular end-diastolic dimension. This is
a measure of the size of the heart at end-diastole.
Large hearts tend to be sick hearts.
8. wall-motion-score -- a measure of how the segments of the left
ventricle are moving
9. wall-motion-index -- equals wall-motion-score divided by number of
segments seen. Usually 12-13 segments are seen
in an echocardiogram. Use this variable INSTEAD
of the wall motion score.
10. mult -- a derivate var which can be ignored
11. name -- the name of the patient (I have replaced them with "name")
12. group -- meaningless, ignore it
13. alive-at-1 -- Boolean-valued. Derived from the first two attributes.
0 means patient was either dead after 1 year or had
been followed for less than 1 year. 1 means patient
was alive at 1 year.
8. Missing Attribute Values: (denoted by "?")
Attribute #: Number of Missing Values: (total: 132)
------------ -------------------------
1 2
2 1
3 5
4 1
5 8
6 15
7 11
8 4
9 1
10 4
11 0
12 22
13 58
9. Distribution of attribute number 2: still-alive
Value Number of instances with this value
---- -----------------------------------
0 88 (dead)
1 43 (alive)
? 1
Total 132
10. Distribution of attribute number 13: alive-at-1
Value Number of instances with this value
---- -----------------------------------
0 50
1 24
? 58
Total 132

class (target) | numeric | 53 unique values 0 missing | |

still_alive | nominal | 2 unique values 0 missing | |

age | numeric | 38 unique values 5 missing | |

pericardial | nominal | 2 unique values 0 missing | |

fractional | numeric | 51 unique values 7 missing | |

epss | numeric | 82 unique values 14 missing | |

lvdd | numeric | 93 unique values 10 missing | |

wall_score | numeric | 44 unique values 3 missing | |

wall_index | numeric | 58 unique values 1 missing | |

alive_at_1 | nominal | 2 unique values 57 missing |

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

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

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

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

2

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

0.17

First quartile of skewness among attributes of the numeric type.

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

0

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

0.45

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

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

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

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

1.02

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

12.19

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

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

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

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

0.73

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

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

2

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

5.04

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

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

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

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

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

2

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

1.62

Third quartile of kurtosis among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

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

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

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

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

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

1.29

Third quartile of skewness among attributes of the numeric type.

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

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0.26

First quartile of kurtosis among attributes of the numeric type.

8.37

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

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

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

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

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

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

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3