Data
pbc

pbc

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Author: Source: Unknown - Please cite: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Case number deleted. X 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. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! NAME: PBC Data SIZE: 418 observations, 20 variables DESCRIPTIVE ABSTRACT: Below is a description of the variables recorded from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. A total of 424 PBC patients, referred to Mayo Clinic during that ten-year interval, met eligibility criteria for the randomized placebo controlled trial of the drug D-penicillamine. The first 312 cases in the data set participated in the randomized trial, and contain largely complete data. The additional 112 cases did not participate in the clinical trial, but consented to have basic measurements recorded and to be followed for survival. Six of those cases were lost to follow-up shortly after diagnosis, so there are data here on an additional 106 cases as well as the 312 randomized participants. Missing data items are denoted by ".". At least one space separates each variable in the .DAT file. Censoring was due to liver transplantation for twenty-five subjects with the following case numbers: 5, 105, 111, 120, 125, 158, 183, 241, 246, 247, 254, 263, 264, 265, 274, 288, 291, 295, 297, 345, 361, 362, 375, 380, 383. SOURCE: Counting Processes and Survival Analysis by T. Fleming & D. Harrington, (1991), published by John Wiley & Sons. VARIABLE DESCRIPTIONS: The data are in free format. That is, at least one blank space separates each variable. The variables contained in the .DAT are: N: Case number. X: The number of days between registration and the earlier of death, liver transplantation, or study analysis time in July, 1986. D: 1 if X is time to death, 0 if time to censoring Z1: Treatment Code, 1 = D-penicillamine, 2 = placebo. Z2: Age in years. For the first 312 cases, age was calculated by dividing the number of days between birth and study registration by 365. Z3: Sex, 0 = male, 1 = female. Z4: Presence of ascites, 0 = no, 1 = yes. Z5: Presence of hepatomegaly, 0 = no, 1 = yes. Z6: Presence of spiders 0 = no, 1 = Yes. Z7: Presence of edema, 0 = no edema and no diuretic therapy for edema; 0.5 = edema present for which no diuretic therapy was given, or edema resolved with diuretic therapy; 1 = edema despite diuretic therapy Z8: Serum bilirubin, in mg/dl. Z9: Serum cholesterol, in mg/dl. Z10: Albumin, in gm/dl. Z11: Urine copper, in mg/day. Z12: Alkaline phosphatase, in U/liter. Z13: SGOT, in U/ml. Z14: Triglycerides, in mg/dl. Z15: Platelet count; coded value is number of platelets per-cubic-milliliter of blood divided by 1000. Z16: Prothrombin time, in seconds. Z17: Histologic stage of disease, graded 1, 2, 3, or 4. STORY BEHIND THE DATA: Between January, 1974 and May, 1984, the Mayo Clinic conducted a double-blinded randomized trial in primary biliary cirrhosis of the liver (PBC), comparing the drug D-penicillamine (DPCA) with a placebo. There were 424 patients who met the eligibility criteria seen at the Clinic while the trial was open for patient registration. Both the treating physician and the patient agreed to participate in the randomized trial in 312 of the 424 cases. The date of randomization and a large number of clinical, biochemical, serologic, and histologic parameters were recorded for each of the 312 clinical trial patients. The data from the trial were analyzed in 1986 for presentation in the clinical literature. For that analysis, disease and survival status as of July, 1986, were recorded for as many patients as possible. By that date, 125 of the 312 patients had died, with only 11 not attributable to PBC. Eight patients had been lost to follow up, and 19 had undergone liver transplantation. PBC is a rare but fatal chronic liver disease of unknown cause, with a prevalence of about 50-cases-per-million population. The primary pathologic event appears to be the destruction of interlobular bile ducts, which may be mediated by immunologic mechanisms. The data discussed here are important in two respects. First, controlled clinical trials are difficult to complete in rare diseases, and this case series of patients uniformly diagnosed, treated, and followed is the largest existing for PBC. The treatment comparison in this trial is more precise than in similar trials having fewer participants and avoids the bias that may arise in comparing a case series to historical controls. Second, the data present an opportunity to study the natural history of the disease. We will see that, despite the immunosuppressive properties of DPCA, there are no detectable differences between the distributions of survival times for the DPCA and placebo treatment groups. This suggests that these groups can be combined in studying the association between survival time from randomization and clinical and other measurements. In the early to mid 1980s, the rate of successful liver transplant increased substantially, and transplant has become an effective therapy for PBC. The Mayo Clinic data set is therefore one of the last allowing a study of the natural history of PBC in patients who were treated with only supportive care or its equivalent. The PBC data can be used to: estimate a survival distribution; test for differences between two groups; and estimate covariate effects via a regression model.

19 features

class (target)numeric399 unique values
0 missing
Dnominal2 unique values
0 missing
Z1nominal2 unique values
106 missing
Z2numeric345 unique values
0 missing
Z3nominal2 unique values
106 missing
Z4nominal2 unique values
106 missing
Z5nominal2 unique values
106 missing
Z6nominal2 unique values
106 missing
Z7nominal3 unique values
0 missing
Z8numeric98 unique values
0 missing
Z9numeric201 unique values
134 missing
Z10numeric154 unique values
0 missing
Z11numeric158 unique values
108 missing
Z12numeric295 unique values
106 missing
Z13numeric179 unique values
106 missing
Z14numeric146 unique values
136 missing
Z15numeric243 unique values
11 missing
Z16numeric48 unique values
2 missing
Z17nominal4 unique values
106 missing

107 properties

418
Number of instances (rows) of the dataset.
19
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
1239
Number of missing values in the dataset.
142
Number of instances with at least one value missing.
11
Number of numeric attributes.
8
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
6
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.38
Average number of distinct values among the attributes of the nominal type.
0.47
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.74
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.67
Mean skewness among attributes of the numeric type.
4.41
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
345.37
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
7.62
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
-0.62
Minimum kurtosis among attributes of the numeric type.
122.56
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
Maximum entropy among attributes.
3.22
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
14.34
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2.22
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1982.66
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
31.58
Percentage of binary attributes.
65.15
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
0.05
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.47
Minimum skewness among attributes of the numeric type.
33.97
Percentage of instances having missing values.
Third quartile of entropy among attributes.
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.
4
The maximum number of distinct values among attributes of the nominal type.
0.42
Minimum standard deviation of attributes of the numeric type.
15.6
Percentage of missing values.
10.04
Third quartile of kurtosis among attributes of the numeric type.
-1003.42
Average class difference between consecutive instances.
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
3.41
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
57.89
Percentage of numeric attributes.
369.51
Third quartile of means among attributes of the numeric type.
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
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2140.39
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
42.11
Percentage of nominal attributes.
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2.72
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
6.02
Mean kurtosis among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.57
First quartile of kurtosis among attributes of the numeric type.
231.94
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
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
449.1
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
10.73
First quartile of means among attributes of the numeric type.
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

6 tasks

10 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
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