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heart-long-beach

heart-long-beach

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Author: V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Source: UCI Please cite: V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:Robert Detrano, M.D., Ph.D. * Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779 * Data Set 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. To see Test Costs (donated by Peter Turney), please see the folder "Costs" * Attribute Information: Only 14 attributes 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") * Relevant Papers: Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64,304--310. David W. Aha & Dennis Kibler. "Instance-based prediction of heart-disease presence with the Cleveland database." Gennari, J.H., Langley, P, & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11--61.

14 features

Class (target)nominal5 unique values
0 missing
V1numeric39 unique values
0 missing
V2numeric2 unique values
0 missing
V3numeric4 unique values
0 missing
V4numeric41 unique values
0 missing
V5numeric100 unique values
0 missing
V6numeric3 unique values
0 missing
V7numeric3 unique values
0 missing
V8numeric60 unique values
0 missing
V9numeric3 unique values
0 missing
V10numeric15 unique values
0 missing
V11numeric4 unique values
0 missing
V12numeric2 unique values
0 missing
V13numeric4 unique values
0 missing
Show all 14 features

107 properties

NumberOfInstances
200
Number of instances (rows) of the dataset.
NumberOfFeatures
14
Number of attributes (columns) of the dataset.
NumberOfClasses
5
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
13
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.22
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.55
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
CfsSubsetEval_DecisionStumpErrRate
0.7
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
CfsSubsetEval_DecisionStumpKappa
0.05
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
CfsSubsetEval_NaiveBayesAUC
0.55
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
CfsSubsetEval_NaiveBayesErrRate
0.7
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
CfsSubsetEval_NaiveBayesKappa
0.05
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
CfsSubsetEval_kNN1NAUC
0.55
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
CfsSubsetEval_kNN1NErrRate
0.7
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
CfsSubsetEval_kNN1NKappa
0.05
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
ClassEntropy
2.17
Entropy of the target attribute values.
DecisionStumpAUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.72
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.07
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.76
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.76
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.76
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
28
Percentage of instances belonging to the most frequent class.
MajorityClassSize
56
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
97.46
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
59.35
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
5
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
9.92
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
29.84
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
9.4
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
11.89
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
5
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.51
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
6.35
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.48
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.74
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
5
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-5.55
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.1
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
5
Percentage of instances belonging to the least frequent class.
MinorityClassSize
10
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.69
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
92.86
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
7.14
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.34
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
1.22
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.41
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.61
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.84
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
2.31
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.39
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
2.2
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
19.16
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.54
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
10.76
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.7
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.7
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.7
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.71
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Classification on heart-long-beach
128 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on heart-long-beach
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Clustering on heart-long-beach
0 runs
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
Clustering on heart-long-beach
0 runs - estimation_procedure: 50 times Clustering
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