Data
postoperative-patient-data

postoperative-patient-data

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 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

9 features

decision (target)nominal3 unique values
0 missing
L-COREnominal3 unique values
0 missing
L-SURFnominal3 unique values
0 missing
L-O2nominal2 unique values
0 missing
L-BPnominal3 unique values
0 missing
SURF-STBLnominal2 unique values
0 missing
CORE-STBLnominal3 unique values
0 missing
BP-STBLnominal3 unique values
0 missing
COMFORTnominal4 unique values
3 missing

19 properties

90
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
3
Number of missing values in the dataset.
3
Number of instances with at least one value missing.
0
Number of numeric attributes.
9
Number of nominal attributes.
22.22
Percentage of binary attributes.
3.33
Percentage of instances having missing values.
0.63
Average class difference between consecutive instances.
0.37
Percentage of missing values.
0.1
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
71.11
Percentage of instances belonging to the most frequent class.
100
Percentage of nominal attributes.
64
Number of instances belonging to the most frequent class.
2.22
Percentage of instances belonging to the least frequent class.
2
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

10 tasks

723 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: decision
309 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: decision
300 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: decision
176 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: decision
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: decision
168 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: decision
77 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: decision
26 runs - estimation_procedure: Interleaved Test then Train - target_feature: decision
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