% 
% 1. Title: Postoperative Patient Data
% 
% 2. Source Information:
%    -- Creators: Sharon Summers, School of Nursing, University of Kansas
%                 Medical Center, Kansas City, KS 66160
%                 Linda Woolery, School of Nursing, University of Missouri,
%                 Columbia, MO 65211
%    -- Donor:    Jerzy W. Grzymala-Busse (jerzy@cs.ukans.edu) (913)864-4488
%    -- Date:     June 1993
% 
% 3. Past Usage:
%    1. A. Budihardjo, J. Grzymala-Busse, L. Woolery (1991). Program LERS_LB 2.5
%       as a tool for knowledge acquisition in nursing, Proceedings of the 4th
%       Int. Conference on Industrial & Engineering Applications of AI & Expert
%       Systems, pp. 735-740.
% 
%    2. L. Woolery, J. Grzymala-Busse, S. Summers, A. Budihardjo (1991). The use
%       of machine learning program LERS_LB 2.5 in knowledge acquisition for 
%       expert system development in nursing. Computers in Nursing 9, pp. 227-234.
% 
% 4. Relevant Information:
%       The classification task of this database is to determine where
%       patients in a postoperative recovery area should be sent to next.  
%       Because hypothermia is a significant concern after surgery
%       (Woolery, L. et. al. 1991), the attributes correspond roughly to body 
%       temperature measurements.
% 
%       Results:
%       -- LERS (LEM2): 48% accuracy
% 
% 5. Number of Instances: 90
% 
% 6. Number of Attributes: 9 including the decision (class attribute)
% 
% 7. Attribute Information:
%      1. L-CORE (patient's internal temperature in C):
%               high (> 37), mid (>= 36 and <= 37), low (< 36)
%      2. L-SURF (patient's surface temperature in C):
%               high (> 36.5), mid (>= 36.5 and <= 35), low (< 35)
%      3. L-O2 (oxygen saturation in %):
%               excellent (>= 98), good (>= 90 and < 98),
%               fair (>= 80 and < 90), poor (< 80)
%      4. L-BP (last measurement of blood pressure):
%               high (> 130/90), mid (<= 130/90 and >= 90/70), low (< 90/70)
%      5. SURF-STBL (stability of patient's surface temperature):
%               stable, mod-stable, unstable
%      6. CORE-STBL (stability of patient's core temperature)
%               stable, mod-stable, unstable
%      7. BP-STBL (stability of patient's blood pressure)
%               stable, mod-stable, unstable
%      8. COMFORT (patient's perceived comfort at discharge, measured as
%               an integer between 0 and 20)
%      9. decision ADM-DECS (discharge decision):
%               I (patient sent to Intensive Care Unit),
%               S (patient prepared to go home),
%               A (patient sent to general hospital floor)
% 
% 8. Missing Attribute Values:
%      Attribute 8 has 3 missing values
% 
% 9. Class Distribution:
%      I (2)
%      S (24)
%      A (64)
% 
% 
% 
% 
%
% Information about the dataset
% CLASSTYPE: nominal
% CLASSINDEX: last
%

@relation postoperative-patient-data

@attribute 'L-CORE' {high,low,mid}
@attribute 'L-SURF' {high,low,mid}
@attribute 'L-O2' {excellent,good}
@attribute 'L-BP' {high,low,mid}
@attribute 'SURF-STBL' {stable,unstable}
@attribute 'CORE-STBL' {mod-stable,stable,unstable}
@attribute 'BP-STBL' {mod-stable,stable,unstable}
@attribute 'COMFORT' {05,07,10,15}
@attribute 'decision' {A,I,S}

@data
mid,low,excellent,mid,stable,stable,stable,15,A
mid,high,excellent,high,stable,stable,stable,10,S
high,low,excellent,high,stable,stable,mod-stable,10,A
mid,low,good,high,stable,unstable,mod-stable,15,A 
mid,mid,excellent,high,stable,stable,stable,10,A
high,low,good,mid,stable,stable,unstable,15,S
mid,low,excellent,high,stable,stable,mod-stable,05,S
high,mid,excellent,mid,unstable,unstable,stable,10,S
mid,high,good,mid,stable,stable,stable,10,S
mid,low,excellent,mid,unstable,stable,mod-stable,10,S
mid,mid,good,mid,stable,stable,stable,15,A
mid,low,good,high,stable,stable,mod-stable,10,A
high,high,excellent,high,unstable,stable,unstable,15,A
mid,high,good,mid,unstable,stable,mod-stable,10,A
mid,low,good,high,unstable,unstable,stable,15,S
high,high,excellent,high,unstable,stable,unstable,10,A
low,high,good,high,unstable,stable,mod-stable,15,A
mid,low,good,high,unstable,stable,stable,10,A
mid,high,good,mid,unstable,stable,unstable,15,A
mid,mid,good,mid,stable,stable,stable,10,A
low,high,good,mid,unstable,stable,stable,15,A
low,mid,excellent,high,unstable,stable,unstable,10,S
mid,mid,good,mid,unstable,stable,unstable,15,A
mid,mid,good,mid,unstable,stable,stable,10,A
high,high,good,mid,stable,stable,mod-stable,10,A
low,mid,good,mid,unstable,stable,stable,10,A
high,mid,good,low,stable,stable,mod-stable,10,A
low,mid,excellent,high,stable,stable,mod-stable,10,A
mid,mid,excellent,mid,stable,stable,unstable,15,A
mid,mid,good,mid,unstable,stable,unstable,10,S
mid,mid,good,high,unstable,stable,stable,10,A
low,low,good,mid,unstable,stable,unstable,10,A
mid,mid,excellent,high,unstable,stable,mod-stable,10,A
mid,low,good,mid,stable,stable,stable,10,A
low,mid,excellent,high,stable,stable,mod-stable,10,A
mid,mid,good,mid,stable,stable,stable,10,A
low,mid,excellent,mid,stable,stable,stable,10,S
low,low,good,mid,unstable,stable,unstable,10,S
low,low,good,mid,stable,stable,stable,07,S
mid,mid,good,high,unstable,stable,mod-stable,10,A
low,low,good,mid,unstable,stable,stable,10,A
low,mid,good,mid,stable,stable,stable,15,S
high,high,good,high,unstable,stable,stable,15,S
mid,mid,good,mid,stable,stable,stable,10,S
low,low,excellent,mid,stable,stable,stable,10,A
low,mid,good,mid,unstable,stable,stable,10,S
low,mid,good,high,unstable,stable,stable,?,I
mid,mid,excellent,mid,unstable,stable,stable,10,A
high,high,excellent,high,stable,stable,unstable,?,A
mid,high,good,low,unstable,stable,stable,10,A
mid,high,good,mid,unstable,mod-stable,mod-stable,10,A
low,high,excellent,mid,unstable,stable,stable,10,A
mid,low,excellent,high,unstable,stable,unstable,10,A
mid,mid,good,mid,unstable,stable,mod-stable,10,S
high,high,excellent,mid,unstable,stable,mod-stable,10,A
mid,mid,good,mid,unstable,stable,stable,15,A
high,mid,good,high,stable,stable,unstable,15,A
mid,low,good,high,unstable,stable,mod-stable,10,A
low,low,good,high,stable,stable,stable,10,A
mid,high,good,mid,stable,stable,mod-stable,10,A
mid,high,good,mid,unstable,stable,unstable,10,A
mid,low,excellent,high,stable,stable,stable,10,A
mid,mid,good,mid,stable,stable,unstable,10,A
mid,low,excellent,mid,stable,stable,unstable,10,S
high,mid,excellent,mid,unstable,unstable,unstable,10,A
mid,mid,good,high,stable,stable,stable,10,S
mid,low,excellent,mid,unstable,stable,stable,10,A
mid,mid,excellent,mid,unstable,stable,stable,10,A
mid,mid,excellent,high,stable,stable,stable,10,A
mid,mid,excellent,low,stable,stable,stable,10,A
mid,low,excellent,mid,unstable,unstable,unstable,?,A
low,low,excellent,mid,stable,stable,stable,10,A
mid,mid,excellent,mid,stable,stable,mod-stable,10,S
mid,mid,excellent,high,stable,stable,stable,10,A
mid,low,excellent,high,stable,stable,mod-stable,10,A
low,mid,good,mid,stable,stable,unstable,10,A
mid,mid,excellent,mid,stable,stable,mod-stable,10,A
mid,mid,excellent,mid,stable,stable,unstable,10,A
mid,mid,excellent,mid,unstable,unstable,stable,10,S
mid,mid,good,high,stable,stable,stable,10,A
mid,mid,excellent,mid,stable,stable,stable,15,A
mid,mid,excellent,mid,stable,stable,stable,10,S
mid,low,good,mid,stable,stable,unstable,10,I
high,mid,excellent,mid,unstable,stable,unstable,05,A
mid,mid,excellent,mid,stable,stable,unstable,10,A
mid,mid,excellent,mid,unstable,stable,stable,10,A
mid,mid,excellent,mid,unstable,stable,stable,15,S
mid,mid,good,mid,unstable,stable,stable,15,A
mid,mid,excellent,mid,unstable,stable,stable,10,A
mid,mid,good,mid,unstable,stable,stable,15,S