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pbcseq

pbcseq

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Author: Source: Unknown - Date unknown Please cite: Primary Biliary Cirrhosis This data set is a follow-up to the original PBC data set, as discussed in appendix D of Fleming and Harrington, Counting Processes and Survival Analysis, Wiley, 1991. An analysis based on the enclised data is found in Murtaugh PA. Dickson ER. Van Dam GM. Malinchoc M. Grambsch PM. Langworthy AL. Gips CH. "Primary biliary cirrhosis: prediction of short-term survival based on repeated patient visits." Hepatology. 20(1.1):126-34, 1994. Quoting from F&H. "The following pages contain the data from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. A description of the clinical background for the trial and the covariates recorded here is in Chapter 0, especially Section 0.2. A more extended discussion can be found in Dickson, et al., Hepatology 10:1-7 (1989) and in Markus, et al., N Eng J of Med 320:1709-13 (1989). "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 the data here are on an additional 106 cases as well as the 312 randomized participants. Missing data items are denoted by `.'. " The F&H data set contains only baseline measurements of the laboratory paramters. This data set contains multiple laboratory results, but only on the first 312 patients. Some baseline data values in this file differ from the original PBC file, for instance, the data errors in prothrombin time and age which were discovered after the orignal analysis, during research work on dfbeta residuals. (These two data points are discussed in F&H, figure 4.6.7). Another major difference is that there was significantly more follow-up for many of the patients at the time this data set was assembled. One "feature" of the data deserves special comment. The last observation before death or liver transplant often has many more missing covariates than other data rows. The original clinical protocol for these patients specified visits at 6 months, 1 year, and annually thereafter. At these protocol visits lab values were obtained for a large pre-specified battery of tests. "Extra" visits, often undertaken because of worsening medical condition, did not necessarily have all this lab work. The missing values are thus potentially informative, and violate the usual "missing at random" (MCAR or MAC) assumptions that are assumed in analyses. Because of the earlier published results on the Mayo PBC risk score, however, the 5 variables involved in that computation were usually obtained, i.e., age, bilirubin, albumin, prothrombin time, and edema score. Variables: case number number of days between registration and the earlier of death, transplantion, or study analysis time status: 0=alive, 1=transplanted, 2=dead drug: 1= D-penicillamine, 0=placebo age in days, at registration sex: 0=male, 1=female day: number of days between enrollment and this visit date, remaining values on the line of data refer to this visit. presence of asictes: 0=no 1=yes presence of hepatomegaly 0=no 1=yes presence of spiders 0=no 1=yes presence of edema 0=no edema and no diuretic therapy for edema; .5 = edema present without diuretics, or edema resolved by diuretics; 1 = edema despite diuretic therapy serum bilirubin in mg/dl serum cholesterol in mg/dl albumin in gm/dl alkaline phosphatase in U/liter SGOT in U/ml (serum glutamic-oxaloacetic transaminase, the enzyme name has subsequently changed to "ALT" in the medical literature) platelets per cubic ml / 1000 prothrombin time in seconds histologic stage of disease Information about the dataset CLASSTYPE: numeric CLASSINDEX: 3

19 features

status (target)numeric3 unique values
0 missing
presence_of_spidersnominal2 unique values
58 missing
histologic_stage_of_diseasenumeric4 unique values
0 missing
prothrombin_timenumeric78 unique values
0 missing
plateletsnumeric414 unique values
73 missing
SGOTnumeric418 unique values
0 missing
alkaline_phosphatasenumeric1263 unique values
60 missing
albuminnumeric254 unique values
0 missing
serum_cholesterolnumeric375 unique values
821 missing
serum_bilirubinnumeric193 unique values
0 missing
presence_of_edemanumeric3 unique values
0 missing
case_numbernumeric312 unique values
0 missing
presence_of_hepatomegalynominal2 unique values
61 missing
presence_of_asictesnominal2 unique values
60 missing
daynominal1024 unique values
0 missing
sexnominal2 unique values
0 missing
agenumeric308 unique values
0 missing
drugnominal2 unique values
0 missing
number_of_daysnumeric305 unique values
0 missing
Show all 19 features

107 properties

NumberOfInstances
1945
Number of instances (rows) of the dataset.
NumberOfFeatures
19
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
1133
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
832
Number of instances with at least one value missing.
NumberOfNumericFeatures
13
Number of numeric attributes.
NumberOfSymbolicFeatures
6
Number of nominal attributes.
AutoCorrelation
0.85
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
77.02
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
17992.08
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
1024
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
6.24
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
3675.03
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
14.13
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
1780.71
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
172.33
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.79
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
506.14
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.78
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.18
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.98
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.32
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
5
Number of binary attributes.
PercentageOfBinaryFeatures
26.32
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
42.78
Percentage of instances having missing values.
PercentageOfMissingValues
3.07
Percentage of missing values.
PercentageOfNumericFeatures
68.42
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
31.58
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.62
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
3.33
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.04
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.91
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
2.69
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
122.67
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.87
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
78.44
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
23.65
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
851.19
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
3.93
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
681.17
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
417.23
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Regression on pbcseq
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: status
Supervised Regression on pbcseq
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: status
Clustering on pbcseq
0 runs
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
Clustering on pbcseq
0 runs - estimation_procedure: 50 times Clustering
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