Data
diabetes

diabetes

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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  • mythbusting_1 OpenML-CC18 OpenML100 study_1 study_123 study_135 study_14 study_15 study_20 study_29 study_30 study_34 study_37 study_41 study_52 study_7 study_70 study_98 study_99 uci
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Author: [Vincent Sigillito](vgs@aplcen.apl.jhu.edu) Source: [Obtained from UCI](https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes) Please cite: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html) 1. Title: Pima Indians Diabetes Database 2. Sources: (a) Original owners: National Institute of Diabetes and Digestive and Kidney Diseases (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu) Research Center, RMI Group Leader Applied Physics Laboratory The Johns Hopkins University Johns Hopkins Road Laurel, MD 20707 (301) 953-6231 (c) Date received: 9 May 1990 3. Past Usage: 1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., & Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In {it Proceedings of the Symposium on Computer Applications and Medical Care} (pp. 261--265). IEEE Computer Society Press. The diagnostic, binary-valued variable investigated is whether the patient shows signs of diabetes according to World Health Organization criteria (i.e., if the 2 hour post-load plasma glucose was at least 200 mg/dl at any survey examination or if found during routine medical care). The population lives near Phoenix, Arizona, USA. Results: Their ADAP algorithm makes a real-valued prediction between 0 and 1. This was transformed into a binary decision using a cutoff of 0.448. Using 576 training instances, the sensitivity and specificity of their algorithm was 76% on the remaining 192 instances. 4. Relevant Information: Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. ADAP is an adaptive learning routine that generates and executes digital analogs of perceptron-like devices. It is a unique algorithm; see the paper for details. 5. Number of Instances: 768 6. Number of Attributes: 8 plus class 7. For Each Attribute: (all numeric-valued) 1. Number of times pregnant 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test 3. Diastolic blood pressure (mm Hg) 4. Triceps skin fold thickness (mm) 5. 2-Hour serum insulin (mu U/ml) 6. Body mass index (weight in kg/(height in m)^2) 7. Diabetes pedigree function 8. Age (years) 9. Class variable (0 or 1) 8. Missing Attribute Values: None 9. Class Distribution: (class value 1 is interpreted as "tested positive for diabetes") Class Value Number of instances 0 500 1 268 10. Brief statistical analysis: Attribute number: Mean: Standard Deviation: 1. 3.8 3.4 2. 120.9 32.0 3. 69.1 19.4 4. 20.5 16.0 5. 79.8 115.2 6. 32.0 7.9 7. 0.5 0.3 8. 33.2 11.8 Relabeled values in attribute 'class' From: 0 To: tested_negative From: 1 To: tested_positive

9 features

class (target)nominal2 unique values
0 missing
pregnumeric17 unique values
0 missing
plasnumeric136 unique values
0 missing
presnumeric47 unique values
0 missing
skinnumeric51 unique values
0 missing
insunumeric186 unique values
0 missing
massnumeric248 unique values
0 missing
pedinumeric517 unique values
0 missing
agenumeric52 unique values
0 missing

107 properties

768
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
8
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.38
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
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.29
First quartile of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.72
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.53
Mean skewness among attributes of the numeric type.
4.5
First quartile of standard deviation of attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.27
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
25.73
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.38
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
0.31
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
65.1
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.97
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.93
Entropy of the target attribute values.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
500
Number of instances belonging to the most frequent class.
-0.52
Minimum kurtosis among attributes of the numeric type.
32.62
Second quartile (Median) of means among attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.47
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
7.21
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.54
Second quartile (Median) of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
120.89
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
11.11
Percentage of binary attributes.
13.86
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-1.84
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.31
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.
2
The maximum number of distinct values among attributes of the nominal type.
0.33
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
5.49
Third quartile of kurtosis among attributes of the numeric type.
0.55
Average class difference between consecutive instances.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.27
Maximum skewness among attributes of the numeric type.
34.9
Percentage of instances belonging to the least frequent class.
88.89
Percentage of numeric attributes.
77.13
Third quartile of means among attributes of the numeric type.
0.72
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
115.24
Maximum standard deviation of attributes of the numeric type.
268
Number of instances belonging to the least frequent class.
11.11
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.27
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
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.72
Third quartile of skewness among attributes of the numeric type.
0.38
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
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.78
Mean kurtosis among attributes of the numeric type.
0.24
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.28
First quartile of kurtosis among attributes of the numeric type.
28.82
Third quartile of standard deviation of attributes of the numeric type.
0.72
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
44.99
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
8.02
First quartile of means among attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.27
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
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

28 tasks

125235 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
67065 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
778 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
373 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
354 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: matthews_correlation_coefficient - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
368 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
210 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
1311 runs - target_feature: class
1306 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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