This section describes main characteristics of the post-operative data set and its attributes:
General information
Post-Operative data set |
Type | Classification | Origin | Real world |
Features | 8 | (Real / Integer / Nominal) | (0 / 0 / 8) |
Classes | 3 |
Missing values? | Yes |
Total instances | 90 |
Instances without missing values | 87 |
Attribute description
Attribute | Domain |
L-CORE | {mid, high, low} |
L-SURF | {low, high, mid} |
L-O2 | {excellent, good} |
L-BP | {mid, high, low} |
SURF-STBL | {stable, unstable} |
CORE-STBL | {stable, unstable, mod-stable} |
BP-STBL | {stable, mod-stable, unstable} |
COMFORT | {15, 10, 05, 07} |
Decision | {A, S, I} |
Additional 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, the attributes correspond roughly to body temperature measurements.
The class label can take one of the following values: I (patient sent to Intensive Care Unit), S (patient prepared to go home), A (patient sent to general hospital floor).
Attributes description:
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)
In this section you can download some files related to the post-operative data set:
- The complete data set already formatted in KEEL formatcan be downloaded from here.
- A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from here.
- A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from here.
- The header file associated to this data set can be downloaded from here.
- This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Post-Operative+Patient.
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