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acute-inflammations

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Publicly available Visibility: public Uploaded 20-05-2015 by Rafael G. Mantovani

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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

Class (target) | nominal | 2 unique values 0 missing | |

V1 | numeric | 44 unique values 0 missing | |

V2 | nominal | 2 unique values 0 missing | |

V3 | nominal | 2 unique values 0 missing | |

V4 | nominal | 2 unique values 0 missing | |

V5 | nominal | 2 unique values 0 missing | |

V6 | nominal | 2 unique values 0 missing |

0.07

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

0.08

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

0.02

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

0.11

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

0.9

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.83

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

0.48

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

2

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

1.82

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

1

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

2

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

0

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

4.92

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

1

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

-1.55

Third quartile of kurtosis among attributes of the numeric type.

1

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

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

1

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

0.43

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

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

0

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

1

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

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

0.11

Third quartile of skewness among attributes of the numeric type.

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

1

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

1

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

-1.55

First quartile of kurtosis among attributes of the numeric type.

1.82

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

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

0

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

0.2

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

0.98

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

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

1

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

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

3.69

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

0.03

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

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

0

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

2

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

0.11

First quartile of skewness among attributes of the numeric type.

0.9

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

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

1.82

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

0.98

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

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

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

-1.55

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

0.9

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

38.72

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

0.98

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

0.92

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