Data

cholesterol

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ARFF
Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn

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Author:Andras Janosi, M.D., William Steinbrunn, M.D., Matthias Pfisterer, M.D., and Robert Detrano, M.D., Ph.D.
Please cite: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.
Heart Disease Databases
Cholesterol treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
in Connectionist-Based Information Systems. Singapore: Springer-Verlag.
This directory contains 4 databases concerning heart disease diagnosis.
All attributes are numeric-valued. The data was collected from the
four following locations:
1. Cleveland Clinic Foundation (cleveland.data)
2. Hungarian Institute of Cardiology, Budapest (hungarian.data)
3. V.A. Medical Center, Long Beach, CA (long-beach-va.data)
4. University Hospital, Zurich, Switzerland (switzerland.data)
Each database has the same instance format. While the databases have 76 raw attributes, only 14 of them are actually used. Thus I've taken the
liberty of making 2 copies of each database: one with all the attributes
and 1 with the 14 attributes actually used in past experiments.
Past Usage:
```
1. Detrano,~R., Janosi,~A., Steinbrunn,~W., Pfisterer,~M., Schmid,~J.,
Sandhu,~S., Guppy,~K., Lee,~S., & Froelicher,~V. (1989). {it
International application of a new probability algorithm for the
diagnosis of coronary artery disease.} {it American Journal of
Cardiology}, {it 64},304--310.
-- International Probability Analysis
-- Address: Robert Detrano, M.D.
Cardiology 111-C
V.A. Medical Center
5901 E. 7th Street
Long Beach, CA 90028
-- Results in percent accuracy: (for 0.5 probability threshold)
Data Name: CDF CADENZA
-- Hungarian 77 74
Long beach 79 77
Swiss 81 81
-- Approximately a 77% correct classification accuracy with a
logistic-regression-derived discriminant function
2. David W. Aha & Dennis Kibler
--
-- Instance-based prediction of heart-disease presence with the
Cleveland database
-- NTgrowth: 77.0% accuracy
-- C4: 74.8% accuracy
3. John Gennari
-- Gennari, J.~H., Langley, P, & Fisher, D. (1989). Models of
incremental concept formation. {it Artificial Intelligence, 40},
11--61.
-- Results:
-- The CLASSIT conceptual clustering system achieved a 78.9% accuracy
on the Cleveland database.
```
Relevant Information:
This database contains 76 attributes, but all published experiments
refer to using a subset of 14 of them. In particular, the Cleveland
database is the only one that has been used by ML researchers to
this date. The "goal" field refers to the presence of heart disease
in the patient. It is integer valued from 0 (no presence) to 4.
Experiments with the Cleveland database have concentrated on simply
attempting to distinguish presence (values 1,2,3,4) from absence (value
0).
The names and social security numbers of the patients were recently
removed from the database, replaced with dummy values.
One file has been "processed", that one containing the Cleveland
database. All four unprocessed files also exist in this directory.
Number of Instances:
Database: # of instances:
Cleveland: 303
Hungarian: 294
Switzerland: 123
Long Beach VA: 200
Number of Attributes: 76 (including the predicted attribute)
Attribute Information:
```
-- Only 14 used
-- 1. #3 (age)
-- 2. #4 (sex)
-- 3. #9 (cp)
-- 4. #10 (trestbps)
-- 5. #12 (chol)
-- 6. #16 (fbs)
-- 7. #19 (restecg)
-- 8. #32 (thalach)
-- 9. #38 (exang)
-- 10. #40 (oldpeak)
-- 11. #41 (slope)
-- 12. #44 (ca)
-- 13. #51 (thal)
-- 14. #58 (num) (the predicted attribute)
-- Complete attribute documentation:
1 id: patient identification number
2 ccf: social security number (I replaced this with a dummy value of 0)
3 age: age in years
4 sex: sex (1 = male; 0 = female)
5 painloc: chest pain location (1 = substernal; 0 = otherwise)
6 painexer (1 = provoked by exertion; 0 = otherwise)
7 relrest (1 = relieved after rest; 0 = otherwise)
8 pncaden (sum of 5, 6, and 7)
9 cp: chest pain type
-- Value 1: typical angina
-- Value 2: atypical angina
-- Value 3: non-anginal pain
-- Value 4: asymptomatic
10 trestbps: resting blood pressure (in mm Hg on admission to the
hospital)
11 htn
12 chol: serum cholestoral in mg/dl
13 smoke: I believe this is 1 = yes; 0 = no (is or is not a smoker)
14 cigs (cigarettes per day)
15 years (number of years as a smoker)
16 fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
17 dm (1 = history of diabetes; 0 = no such history)
18 famhist: family history of coronary artery disease (1 = yes; 0 = no)
19 restecg: resting electrocardiographic results
-- Value 0: normal
-- Value 1: having ST-T wave abnormality (T wave inversions and/or ST
elevation or depression of > 0.05 mV)
-- Value 2: showing probable or definite left ventricular hypertrophy
by Estes' criteria
20 ekgmo (month of exercise ECG reading)
21 ekgday(day of exercise ECG reading)
22 ekgyr (year of exercise ECG reading)
23 dig (digitalis used furing exercise ECG: 1 = yes; 0 = no)
24 prop (Beta blocker used during exercise ECG: 1 = yes; 0 = no)
25 nitr (nitrates used during exercise ECG: 1 = yes; 0 = no)
26 pro (calcium channel blocker used during exercise ECG: 1 = yes; 0 = no)
27 diuretic (diuretic used used during exercise ECG: 1 = yes; 0 = no)
28 proto: exercise protocol
1 = Bruce
2 = Kottus
3 = McHenry
4 = fast Balke
5 = Balke
6 = Noughton
7 = bike 150 kpa min/min (Not sure if "kpa min/min" is what was
written!)
8 = bike 125 kpa min/min
9 = bike 100 kpa min/min
10 = bike 75 kpa min/min
11 = bike 50 kpa min/min
12 = arm ergometer
29 thaldur: duration of exercise test in minutes
30 thaltime: time when ST measure depression was noted
31 met: mets achieved
32 thalach: maximum heart rate achieved
33 thalrest: resting heart rate
34 tpeakbps: peak exercise blood pressure (first of 2 parts)
35 tpeakbpd: peak exercise blood pressure (second of 2 parts)
36 dummy
37 trestbpd: resting blood pressure
38 exang: exercise induced angina (1 = yes; 0 = no)
39 xhypo: (1 = yes; 0 = no)
40 oldpeak = ST depression induced by exercise relative to rest
41 slope: the slope of the peak exercise ST segment
-- Value 1: upsloping
-- Value 2: flat
-- Value 3: downsloping
42 rldv5: height at rest
43 rldv5e: height at peak exercise
44 ca: number of major vessels (0-3) colored by flourosopy
45 restckm: irrelevant
46 exerckm: irrelevant
47 restef: rest raidonuclid (sp?) ejection fraction
48 restwm: rest wall (sp?) motion abnormality
0 = none
1 = mild or moderate
2 = moderate or severe
3 = akinesis or dyskmem (sp?)
49 exeref: exercise radinalid (sp?) ejection fraction
50 exerwm: exercise wall (sp?) motion
51 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
52 thalsev: not used
53 thalpul: not used
54 earlobe: not used
55 cmo: month of cardiac cath (sp?) (perhaps "call")
56 cday: day of cardiac cath (sp?)
57 cyr: year of cardiac cath (sp?)
58 num: diagnosis of heart disease (angiographic disease status)
-- Value 0: < 50% diameter narrowing
-- Value 1: > 50% diameter narrowing
(in any major vessel: attributes 59 through 68 are vessels)
59 lmt
60 ladprox
61 laddist
62 diag
63 cxmain
64 ramus
65 om1
66 om2
67 rcaprox
68 rcadist
69 lvx1: not used
70 lvx2: not used
71 lvx3: not used
72 lvx4: not used
73 lvf: not used
74 cathef: not used
75 junk: not used
76 name: last name of patient
(I replaced this with the dummy string "name")
```
Missing Attribute Values: Several. Distinguished with value -9.0.
Class Distribution:
```
Database: 0 1 2 3 4 Total
Cleveland: 164 55 36 35 13 303
Hungarian: 188 37 26 28 15 294
Switzerland: 8 48 32 30 5 123
Long Beach VA: 51 56 41 42 10 200
```

chol (target) | numeric | 152 unique values 0 missing | |

age | numeric | 41 unique values 0 missing | |

sex | nominal | 2 unique values 0 missing | |

cp | nominal | 4 unique values 0 missing | |

trestbps | numeric | 50 unique values 0 missing | |

fbs | nominal | 2 unique values 0 missing | |

restecg | nominal | 3 unique values 0 missing | |

thalach | numeric | 91 unique values 0 missing | |

exang | nominal | 2 unique values 0 missing | |

oldpeak | numeric | 40 unique values 0 missing | |

slope | nominal | 3 unique values 0 missing | |

ca | numeric | 4 unique values 4 missing | |

thal | nominal | 3 unique values 2 missing | |

num | numeric | 5 unique values 0 missing |

54.44

Second quartile (Median) of means among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

Minimal mutual information between the nominal attributes and the target attribute.

1.06

Second quartile (Median) of skewness among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

2

The minimal number of distinct values among attributes of the nominal type.

9.04

Second quartile (Median) of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Maximum mutual information between the nominal attributes and the target attribute.

4

The maximum number of distinct values among attributes of the nominal type.

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

1.58

Third quartile of kurtosis among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Third quartile of mutual information between the nominal attributes and the target attribute.

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

1.19

Third quartile of skewness among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

-0.14

First quartile of kurtosis among attributes of the numeric type.

22.88

Third quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Average mutual information between the nominal attributes and the target attribute.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

First quartile of mutual information between the nominal attributes and the target attribute.

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

2.71

Average number of distinct values among the attributes of the nominal type.

-0.21

First quartile of skewness among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.76

Standard deviation of the number of distinct values among attributes of the nominal type.

1.16

First quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.26

Second quartile (Median) of kurtosis among attributes of the numeric type.