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acute-inflammations

acute-inflammations

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Author: Jacek Czerniak Source: UCI Please cite: J.Czerniak, H.Zarzycki, Application of rough sets in the presumptive diagnosis of urinary system diseases, Artificial Intelligence and Security in Computing Systems, ACS'2002 9th International Conference Proceedings, Kluwer Academic Publishers,2003, pp. 41-51. * Abstract: The data was created by a medical expert as a data set to test the expert system, which will perform the presumptive diagnosis of two diseases of the urinary system. * Source: Jacek Czerniak, Ph.D., Assistant Professor Systems Research Institute Polish Academy of Sciences Laboratory of Intelligent Systems ul. Newelska 6, Room 218 01-447 Warszawa, Poland e-mail: jacek.czerniak 'at' ibspan.waw.pl or jczerniak 'at' ukw.edu.pl * Data Set Information: The main idea of this data set is to prepare the algorithm of the expert system, which will perform the presumptive diagnosis of two diseases of urinary system. It will be the example of diagnosing of the acute inflammations of urinary bladder and acute nephritises. For better understanding of the problem let us consider definitions of both diseases given by medics. Acute inflammation of urinary bladder is characterised by sudden occurrence of pains in the abdomen region and the urination in form of constant urine pushing, micturition pains and sometimes lack of urine keeping. Temperature of the body is rising, however most often not above 38C. The excreted urine is turbid and sometimes bloody. At proper treatment, symptoms decay usually within several days. However, there is inclination to returns. At persons with acute inflammation of urinary bladder, we should expect that the illness will turn into protracted form. Acute nephritis of renal pelvis origin occurs considerably more often at women than at men. It begins with sudden fever, which reaches, and sometimes exceeds 40C. The fever is accompanied by shivers and one- or both-side lumbar pains, which are sometimes very strong. Symptoms of acute inflammation of urinary bladder appear very often. Quite not infrequently there are nausea and vomiting and spread pains of whole abdomen. The data was created by a medical expert as a data set to test the expert system, which will perform the presumptive diagnosis of two diseases of urinary system. The basis for rules detection was Rough Sets Theory. Each instance represents an potential patient. Each line of the data file starts with a digit which tells the temperature of patient. -- Attribute lines: For example, '35,9 no no yes yes yes yes no' Where: '35,9' Temperature of patient 'no' Occurrence of nausea 'no' Lumbar pain 'yes' Urine pushing (continuous need for urination) 'yes' Micturition pains 'yes' Burning of urethra, itch, swelling of urethra outlet 'yes' decision: Inflammation of urinary bladder 'no' decision: Nephritis of renal pelvis origin * Attribute Information: a1 Temperature of patient { 35C-42C } a2 Occurrence of nausea { yes, no } a3 Lumbar pain { yes, no } a4 Urine pushing (continuous need for urination) { yes, no } a5 Micturition pains { yes, no } a6 Burning of urethra, itch, swelling of urethra outlet { yes, no } d1 decision: Inflammation of urinary bladder { yes, no } d2 decision: Nephritis of renal pelvis origin { yes, no } * Relevant Papers: J.Czerniak, H.Zarzycki, Application of rough sets in the presumptive diagnosis of urinary system diseases, Artificial Intelligence and Security in Computing Systems, ACS'2002 9th International Conference Proceedings, Kluwer Academic Publishers,2003, pp. 41-51

7 features

Class (target)nominal2 unique values
0 missing
V1numeric44 unique values
0 missing
V2nominal2 unique values
0 missing
V3nominal2 unique values
0 missing
V4nominal2 unique values
0 missing
V5nominal2 unique values
0 missing
V6nominal2 unique values
0 missing
Show all 7 features

107 properties

NumberOfInstances
120
Number of instances (rows) of the dataset.
NumberOfFeatures
7
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
1
Number of numeric attributes.
NumberOfSymbolicFeatures
6
Number of nominal attributes.
AutoCorrelation
0.84
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
1
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
0
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
1
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
1
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
0
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
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
0
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
1
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
0.98
Entropy of the target attribute values.
DecisionStumpAUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.06
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
4.92
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
58.33
Percentage of instances belonging to the most frequent class.
MajorityClassSize
70
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
-1.55
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
38.72
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.48
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.11
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1.82
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0.94
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-1.55
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
38.72
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.2
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
3.69
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.11
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1.82
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.8
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.55
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
38.72
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.02
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
0.11
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1.82
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
41.67
Percentage of instances belonging to the least frequent class.
MinorityClassSize
50
Number of instances belonging to the least frequent class.
NaiveBayesAUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
6
Number of binary attributes.
PercentageOfBinaryFeatures
85.71
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
14.29
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
85.71
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.86
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.55
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
38.72
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.11
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.82
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0.98
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-1.55
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
38.72
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.07
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.11
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1.82
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
0.99
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-1.55
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
38.72
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.43
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.11
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1.82
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Classification on acute-inflammations
360 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on acute-inflammations
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Clustering on acute-inflammations
0 runs
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
Clustering on acute-inflammations
0 runs - estimation_procedure: 50 times Clustering
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