acute: Real categorical data: acute

In CoModes: This package performs clustering for categorical dataset by using a conditional modes mixture model

Description

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. Here the first variable has been modified to only take integer value.

Details

Informations are available here: http://archive.ics.uci.edu/ml/datasets/Acute+Inflammations

• obs: Patients described by six variables Temperature of patient (35C-42C), occurrence of nausea (yes,no), lumbar pain (yes,no), urine pushing (continuous need for urination) (yes,no), mMicturition pains (yes,no), burning of urethra, itch, swelling of urethra outlet (yes,no)

• urin: Inflammation of urinary bladder (yes,no)

• renal: Nephritis of renal pelvis origin (yes,no)

• urinrenal: Inflammation of urinary bladder and Nephritis of renal pelvis origin (1:no-no, 2:yes-no, 3:no-yes, 4:yes-yes)

References

J.Czerniak, H.Zarzycki, Application of rough sets in the presumptive diagnosis of urinary system diseases, Artifical Inteligence and Security in Computing Systems, ACS'2002 9th International Conference Proceedings, Kluwer Academic Publishers,2003, pp. 41-51

Examples

  data(acute)

CoModes documentation built on May 2, 2019, 5:49 p.m.