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heart-c

heart-c

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Author: Source: Unknown - Please cite: Publication Request: >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> This file describes the contents of the heart-disease directory. 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. The authors of the databases have requested: ...that any publications resulting from the use of the data include the names of the principal investigator responsible for the data collection at each institution. They would be: 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Thanks in advance for abiding by this request. David Aha July 22, 1988 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 1. Title: Heart Disease Databases 2. Source Information: (a) Creators: -- 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. -- 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. -- 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. -- 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779 (c) Date: July, 1988 3. 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. 4. 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. 5. Number of Instances: Database: # of instances: Cleveland: 303 Hungarian: 294 Switzerland: 123 Long Beach VA: 200 6. Number of Attributes: 76 (including the predicted attribute) 7. 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") 9. Missing Attribute Values: Several. Distinguished with value -9.0. 10. 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 'slope' is ordered Relabeled values in attribute 'sex' From: 0 To: female From: 1 To: male Relabeled values in attribute 'cp' From: 1 To: typ_angina From: 4 To: asympt From: 3 To: non_anginal From: 2 To: atyp_angina Relabeled values in attribute 'fbs' From: 1 To: t From: 0 To: f Relabeled values in attribute 'restecg' From: 2 To: left_vent_hyper From: 0 To: normal From: 1 To: st_t_wave_abnormality Relabeled values in attribute 'exang' From: 0 To: no From: 1 To: yes Relabeled values in attribute 'slope' From: 1 To: up From: 2 To: flat From: 3 To: down Relabeled values in attribute 'thal' From: 6 To: fixed_defect From: 3 To: normal From: 7 To: reversable_defect Relabeled values in attribute 'num' From: '0' To: '<50' From: '1' To: '>50_1' From: '2' To: '>50_2' From: '3' To: '>50_3' From: '4' To: '>50_4'

14 features

num (target)nominal2 unique values
0 missing
agenumeric41 unique values
0 missing
sexnominal2 unique values
0 missing
cpnominal4 unique values
0 missing
trestbpsnumeric49 unique values
0 missing
cholnumeric152 unique values
0 missing
fbsnominal2 unique values
0 missing
restecgnominal3 unique values
0 missing
thalachnumeric91 unique values
0 missing
exangnominal2 unique values
0 missing
oldpeaknumeric40 unique values
0 missing
slopenominal3 unique values
0 missing
canumeric4 unique values
5 missing
thalnominal3 unique values
2 missing

119 properties

303
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
5
Number of distinct values of the target attribute (if it is nominal).
7
Number of missing values in the dataset.
7
Number of instances with at least one value missing.
6
Number of numeric attributes.
8
Number of nominal attributes.
0.61
Minimal entropy among attributes.
-0.05
First quartile of skewness among attributes of the numeric type.
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.54
The predictive accuracy obtained by always predicting the majority class.
4811.61
Maximum kurtosis among attributes of the numeric type.
1.05
First quartile of standard deviation of attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.05
Number of attributes divided by the number of instances.
0.76
Standard deviation of the number of distinct values among attributes of the nominal type.
13207.13
Maximum of means among attributes of the numeric type.
1.09
Second quartile (Median) of entropy among attributes.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
9.16
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.21
Maximum mutual information between the nominal attributes and the target attribute.
-1.82
Minimum kurtosis among attributes of the numeric type.
0.8
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.79
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
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.22
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
4
The maximum number of distinct values among attributes of the nominal type.
-23.5
Minimum of means among attributes of the numeric type.
11.82
Second quartile (Median) of means among attributes of the numeric type.
0.23
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
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
67.4
Maximum skewness among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.12
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.54
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
0.26
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
7947.07
Maximum standard deviation of attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0.58
Second quartile (Median) of skewness among attributes of the numeric type.
0.79
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
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.11
Average entropy of the attributes.
-21.17
Minimum skewness among attributes of the numeric type.
21.43
Percentage of binary attributes.
5.57
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.23
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
0.28
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Minimum standard deviation of attributes of the numeric type.
2.31
Percentage of instances having missing values.
1.29
Third quartile of entropy among attributes.
0.54
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
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.46
Percentage of instances belonging to the least frequent class.
0.17
Percentage of missing values.
7.97
Third quartile of kurtosis among attributes of the numeric type.
0.79
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
35.73
Mean kurtosis among attributes of the numeric type.
0
Number of instances belonging to the least frequent class.
42.86
Percentage of numeric attributes.
45.25
Third quartile of means among attributes of the numeric type.
0.23
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
0.28
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
108.58
Mean of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
57.14
Percentage of nominal attributes.
0.2
Third quartile of mutual information between the nominal attributes and the target attribute.
0.54
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
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
54.46
Percentage of instances belonging to the most frequent class.
0.11
Average mutual information between the nominal attributes and the target attribute.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.9
First quartile of entropy among attributes.
2.28
Third quartile of skewness among attributes of the numeric type.
0.99
Entropy of the target attribute values.
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
165
Number of instances belonging to the most frequent class.
2.71
Average number of distinct values among the attributes of the nominal type.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.19
First quartile of kurtosis among attributes of the numeric type.
19.51
Third quartile of standard deviation of attributes of the numeric type.
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.28
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1.74
Maximum entropy among attributes.
1.76
Mean skewness among attributes of the numeric type.
9.27
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.28
First quartile of means among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.25
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
40.75
Mean standard deviation of attributes of the numeric type.
3
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
0.54
Average class difference between consecutive instances.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

10 tasks

680 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: num
323 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: num
312 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: num
189 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: num
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: num
177 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: num
75 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: num
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: num
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