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pharynx

pharynx

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Author: Source: Unknown - Please cite: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Case number deleted. 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: Pharynx (A clinical Trial in the Trt. of Carcinoma of the Oropharynx). SIZE: 195 observations, 13 variables. DESCRIPTIVE ABSTRACT: The .dat file gives the data for a part of a large clinical trial carried out by the Radiation Therapy Oncology Group in the United States. The full study included patients with squamous carcinoma of 15 sites in the mouth and throat, with 16 participating institutions, though only data on three sites in the oropharynx reported by the six largest institutions are considered here. Patients entering the study were randomly assigned to one of two treatment groups, radiation therapy alone or radiation therapy together with a chemotherapeutic agent. One objective of the study was to compare the two treatment policies with respect to patient survival. SOURCE: The Statistical Analysis of Failure Time Data, by JD Kalbfleisch & RL Prentice, (1980), Published by John Wiley & Sons VARIABLE DESCRIPTIONS: The data are in free format. That is, at least one blank space separates each variable in the .dat file. The variables are as follows: Case: Case Number Inst: Participating Institution sex: 1=male, 2=female Treatment: 1=standard, 2=test Grade: 1=well differentiated, 2=moderately differentiated, 3=poorly differentiated, 9=missing Age: In years at time of diagnosis Condition: 1=no disability, 2=restricted work, 3=requires assistance with self care, 4=bed confined, 9=missing Site: 1=faucial arch, 2=tonsillar fossa, 3=posterior pillar, 4=pharyngeal tongue, 5=posterior wall T staging: 1=primary tumor measuring 2 cm or less in largest diameter, 2=primary tumor measuring 2 cm to 4 cm in largest diameter with minimal infiltration in depth, 3=primary tumor measuring more than 4 cm, 4=massive invasive tumor N staging: 0=no clinical evidence of node metastases, 1=single positive node 3 cm or less in diameter, not fixed, 2=single positive node more than 3 cm in diameter, not fixed, 3=multiple positive nodes or fixed positive nodes Entry Date: Date of study entry: Day of year and year Status: 0=censored, 1=dead Time: Survival time in days from day of diagnosis STORY BEHIND THE DATA: Approximately 30% of the survival times are censored owing primarily to patients surviving to the time of analysis. Some patients were lost to follow-up because the patient moved or transferred to an institution not participating in the study, though these cases were relatively rare. From a statistical point of view, an important feature of these data is the considerable lack of homogeneity between individuals being studied. Of course, as part of the study design, certain criteria for patient eligibility had to be met which eliminated extremes in the extent of disease, but still many factors are not controlled. This study included measurements of many covariates which would be expected to relate to survival experience. Six such variables are given in the data (sex, T staging, N staging, age, general condition, and grade). The site of the primary tumor and possible differences between participating institutions require consideration as well. The T,N staging classification gives a measure of the extent of the tumor at the primary site and at regional lymph nodes. T=1, refers to a small primary tumor, 2 centimeters or less in largest diameter, whereas T=4 is a massive tumor with extension to adjoining tissue. T=2 and T=3 refer to intermediate cases. N=0 refers to there being no clinical evidence of a lymph node metastasis and N=1, N=2, N=3 indicate, in increasing magnitude, the extent of existing lymph node involvement. Patients with classifications T=1,N=0; T=1,N=1; T=2,N=0; or T=2,N=1, or with distant metastases were excluded from study. The variable general condition gives a measure of the functional capacity of the patient at the time of diagnosis (1 refers to no disability whereas 4 denotes bed confinement; 2 and 3 measure intermediate levels). The variable grade is a measure of the degree of differentiation of the tumor (the degree to which the tumor cell resembles the host cell) from 1 (well differentiated) to 3 (poorly differentiated) In addition to the primary question whether the combined treatment mode is preferable to the conventional radiation therapy, it is of considerable interest to determine the extent to which the several covariates relate to subsequent survival. It is also imperative in answering the primary question to adjust the survivals for possible imbalance that may be present in the study with regard to the other covariates. Such problems are similar to those encountered in the classical theory of linear regression and the analysis of covariance. Again, the need to accommodate censoring is an important distinguishing point. In many situations it is also important to develop nonparametric and robust procedures since there is frequently little empirical or theoretical work to support a particular family of failure time distributions.

11 features

class (target)numeric177 unique values
0 missing
Instnominal6 unique values
0 missing
sexnominal2 unique values
0 missing
Treatmentnominal2 unique values
0 missing
Gradenominal3 unique values
1 missing
Agenumeric48 unique values
0 missing
Conditionnominal5 unique values
1 missing
Sitenominal3 unique values
0 missing
Tnominal4 unique values
0 missing
Nnominal4 unique values
0 missing
Entry (ignore)nominal184 unique values
0 missing
Statusnominal2 unique values
0 missing
Show all 11 features

107 properties

NumberOfInstances
195
Number of instances (rows) of the dataset.
NumberOfFeatures
11
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
2
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
2
Number of instances with at least one value missing.
NumberOfNumericFeatures
2
Number of numeric attributes.
NumberOfSymbolicFeatures
9
Number of nominal attributes.
AutoCorrelation
-455.47
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
0.06
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
0.37
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
558.73
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
184
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.06
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
418.72
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.33
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
309.58
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
21.5
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.52
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
214.97
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
0.28
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
60.44
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
-0.03
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
11.22
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
3
Number of binary attributes.
PercentageOfBinaryFeatures
27.27
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
1.03
Percentage of instances having missing values.
PercentageOfMissingValues
0.09
Percentage of missing values.
PercentageOfNumericFeatures
18.18
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
81.82
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.28
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
60.44
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
-0.03
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
11.22
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.33
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
309.58
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
0.52
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
214.97
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.37
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
558.73
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
1.06
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
418.72
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
57.11
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

18 tasks

Supervised Regression on pharynx
10 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
Supervised Regression on pharynx
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on pharynx
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
Supervised Regression on pharynx
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on pharynx
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on pharynx
0 runs - estimation_procedure: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Regression on pharynx
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class
Clustering on pharynx
0 runs
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
Clustering on pharynx
0 runs - estimation_procedure: 50 times Clustering
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