不好意思.麻煩各位唷
因為學校老師要我們做Data Mining的作業
要先自己去找數據再自己找軟體去跑><
以下是原文


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 ([email protected]) (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)


以下是我翻譯的.

1. Title: Postoperative Patient Data

2. Source Information:
--Creators: Sharon Summers,看護學校,美國堪薩斯洲醫療中心大學,美國堪薩斯洲城市,KS 66160
Linda Woolery,看護學校,密蘇里大學,美洲,MO 65211
--Donor: Jerzy W. Grzymala-Busse ([email protected]) ( 913 ) 864-4488
--Date: June 1993

3. Past Usage:
1 .A Budihardjo , J. Grzymala - Busse , L. Woolery ( 1991 ).
設計LERS_LB 2.5做為看護在知識取得時的工具,Proceeding of the 4th Int. 討論在AI人工智慧和專家系統中的工業及工程學上的應用, pp. 735-740
2 .L. Woolery , J. Grzymala - Busse , S. Summers, A. Budihardjo ( 1991 ).
看護利用學習機器設計LERS_LB 2.5在專家系統發展知識取得

4. Relevant Information:
這個資料庫的分類任務是要決定手術後的病人應該送至那個何適的地區。
因為低體溫症是一個在手術後值得去關心的重要事情
( Woolery , L. et. al. 1991 ) , 這個屬性大致上對體溫測量一致。
Results:
-- LERS ( LEM2 ) : 48% accuracy

5. Number of Instances: 90

6. Number of Attribute: 9 包括決定 (班級(課)屬性)

7. Attribute Information:
1 。 L - CORE (病人在C中的原來溫度) : high (> 37) , mid (> = 36 和< = 37 ) , low (< 36 )
2 。 L - SURF (病人在C中的表面溫度) : high (> 36.5) , mid (> = 36.5 和< = 35 ) , low (< 35 )
3 。 L - O2 (氧氣飽和百分比) : excellent (> = 98) , good(> = 90 和< 98 ),fair(> = 80 和< 90 ) ,poor(<80 )
4 。 L - BP (最近的血壓測量) : high (> 130 / 90) , mid (< = 130 / 90 和> = 90 / 70 ) , low (< 90 / 70 )
5 。 SURF - STBL (病人表面溫度的穩定性) : stable,mod-stable,unstable
6 。 CORE - STBL (病人核心溫度的穩定性): stable,mod-stable,unstable
7 。 BP - STBL (病人血壓的穩定性) : stable,mod-stable,unstable
8 。 COMFORT (病人的處於下貨(排液)狀態的感知的舒適, 作為 0 和 20 之間的整數測量了)
9 。 decision ADM- DECS (況狀決定) :
I (病人送到加護病房) ,
S (病人準備回家) ,
A (病人送到一般病房)

8. Missing Attribute Values:
Attribute 8 has 3 missing values

9. Class Distribution:
I(2)
S(24)
A(64)

其實最難的部分為要如何去找適合軟體來跑..
實在頭痛~希望能幫上忙的朋友能幫一下唷
謝謝各位