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cholesterol

cholesterol

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Author: Source: Unknown - Please cite: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 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. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 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

14 features

chol (target)numeric152 unique values
0 missing
agenumeric41 unique values
0 missing
sexnominal2 unique values
0 missing
cpnominal4 unique values
0 missing
trestbpsnumeric50 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
4 missing
thalnominal3 unique values
2 missing
numnumeric5 unique values
0 missing
Show all 14 features

107 properties

NumberOfInstances
303
Number of instances (rows) of the dataset.
NumberOfFeatures
14
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
6
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
6
Number of instances with at least one value missing.
NumberOfNumericFeatures
7
Number of numeric attributes.
NumberOfSymbolicFeatures
7
Number of nominal attributes.
AutoCorrelation
-58.06
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
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
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
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
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
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
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
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
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
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.05
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
4.49
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
246.69
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
4
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.27
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
51.78
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.93
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
83.58
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
2.71
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.66
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
14.95
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.52
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.67
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.54
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.94
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
3
Number of binary attributes.
PercentageOfBinaryFeatures
21.43
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
1.98
Percentage of instances having missing values.
PercentageOfMissingValues
0.14
Percentage of missing values.
PercentageOfNumericFeatures
50
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
50
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.14
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.94
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
-0.21
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.16
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.26
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
54.44
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
1.06
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
9.04
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.58
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
149.61
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
1.19
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
22.88
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0.76
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Regression on cholesterol
175 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: chol
Supervised Regression on cholesterol
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: chol
Clustering on cholesterol
0 runs
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
Clustering on cholesterol
0 runs - estimation_procedure: 50 times Clustering
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