Data
acute-inflammations

acute-inflammations

active ARFF Publicly available Visibility: public Uploaded 20-05-2015 by Rafael G. Mantovani
0 likes downloaded by 11 people , 14 total downloads 0 issues 0 downvotes
  • study_127 study_50 study_52 study_7 study_88
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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

62 properties

120
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
1
Number of numeric attributes.
6
Number of nominal attributes.
85.71
Percentage of binary attributes.
1.82
Second quartile (Median) of standard deviation of attributes of the numeric type.
1
Maximum entropy among attributes.
-1.55
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
0.99
Third quartile of entropy among attributes.
-1.55
Maximum kurtosis among attributes of the numeric type.
38.72
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-1.55
Third quartile of kurtosis among attributes of the numeric type.
38.72
Maximum of means among attributes of the numeric type.
0.02
Minimal mutual information between the nominal attributes and the target attribute.
14.29
Percentage of numeric attributes.
38.72
Third quartile of means among attributes of the numeric type.
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.
85.71
Percentage of nominal attributes.
0.43
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
0.11
Minimum skewness among attributes of the numeric type.
0.86
First quartile of entropy among attributes.
0.11
Third quartile of skewness among attributes of the numeric type.
0.11
Maximum skewness among attributes of the numeric type.
1.82
Minimum standard deviation of attributes of the numeric type.
-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.
1.82
Maximum standard deviation of attributes of the numeric type.
41.67
Percentage of instances belonging to the least frequent class.
38.72
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.94
Average entropy of the attributes.
50
Number of instances belonging to the least frequent class.
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
-1.55
Mean kurtosis among attributes of the numeric type.
6
Number of binary attributes.
0.11
First quartile of skewness among attributes of the numeric type.
38.72
Mean of means among attributes of the numeric type.
1.82
First quartile of standard deviation of attributes of the numeric type.
0.84
Average class difference between consecutive instances.
0.2
Average mutual information between the nominal attributes and the target attribute.
0.98
Second quartile (Median) of entropy among attributes.
0.98
Entropy of the target attribute values.
3.69
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-1.55
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.06
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
38.72
Second quartile (Median) of means among attributes of the numeric type.
4.92
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.11
Mean skewness among attributes of the numeric type.
0.07
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
58.33
Percentage of instances belonging to the most frequent class.
1.82
Mean standard deviation of attributes of the numeric type.
0.11
Second quartile (Median) of skewness among attributes of the numeric type.
70
Number of instances belonging to the most frequent class.
0.8
Minimal entropy among attributes.

5 tasks

360 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task