Data
diabetes

diabetes

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • Health Kaggle mythbusting_1 Nutrition 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
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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
Show all 9 features

107 properties

NumberOfInstances
768
Number of instances (rows) of the dataset.
NumberOfFeatures
9
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
8
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
MaxStdDevOfNumericAtts
115.24
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
34.9
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
88.89
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
77.13
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
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
RandomTreeDepth2AUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
268
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
11.11
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
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
RandomTreeDepth2ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanKurtosisOfNumericAtts
2.78
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
1.72
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
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
RandomTreeDepth2Kappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
44.99
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
0.28
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
28.82
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
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
RandomTreeDepth3AUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
8.02
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
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
RandomTreeDepth3ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
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
RandomTreeDepth3Kappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
-0.29
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NAUC
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
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
0.53
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
4.5
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NErrRate
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
kNN1NAUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanStdDevOfNumericAtts
25.73
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
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
kNN1NErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
65.1
Percentage of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
1.97
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
0.93
Entropy of the target attribute values.
kNN1NKappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
500
Number of instances belonging to the most frequent class.
MinKurtosisOfNumericAtts
-0.52
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
32.62
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
Maximum entropy among attributes.
MinMeansOfNumericAtts
0.47
Minimum of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
REPTreeDepth3ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
7.21
Maximum kurtosis among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.54
Second quartile (Median) of skewness among attributes of the numeric type.
REPTreeDepth3Kappa
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
120.89
Maximum of means among attributes of the numeric type.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
11.11
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
13.86
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1AUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0.01
Number of attributes divided by the number of instances.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinSkewnessOfNumericAtts
-1.84
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
RandomTreeDepth1ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MinStdDevOfNumericAtts
0.33
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
5.49
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.55
Average class difference between consecutive instances.
RandomTreeDepth1Kappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
2.27
Maximum skewness among attributes of the numeric type.
Show all 107 properties

50 tasks

Supervised Classification on diabetes
131686 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on diabetes
67074 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on diabetes
781 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on diabetes
373 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on diabetes
354 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on diabetes
31 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on diabetes
16 runs - estimation_procedure: 10% Holdout set - target_feature: class
Supervised Classification on diabetes
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on diabetes
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: matthews_correlation_coefficient - target_feature: class
Supervised Classification on diabetes
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on diabetes
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: confusion_matrix - target_feature: class
Supervised Classification on diabetes
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on diabetes
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: root_mean_squared_error - target_feature: class
Supervised Classification on diabetes
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on diabetes
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Supervised Regression on diabetes
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: age
Learning Curve on diabetes
368 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on diabetes
231 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: mean_weighted_precision - target_feature: class
Learning Curve on diabetes
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on diabetes
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - target_feature: class
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering - target_feature: test
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Clustering on diabetes
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on diabetes
1311 runs - target_feature: class
Subgroup Discovery on diabetes
1306 runs - target_feature: class
Subgroup Discovery on diabetes
0 runs - target_feature: class
Subgroup Discovery on diabetes
0 runs - target_feature: class
Subgroup Discovery on diabetes
0 runs - target_feature: class
Subgroup Discovery on diabetes
0 runs - target_feature: class
Subgroup Discovery on diabetes
0 runs - target_feature: class
Subgroup Discovery on diabetes
0 runs - target_feature: class
Define a new task