Data
Diabetes130US

Diabetes130US

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Author: Attila Reiss, Department Augmented Vision, DFKI, Germany, "attila.reiss '@' dfki.de Date: August 2012. Source: UCI Please cite: Beata Strack, Jonathan P. DeShazo, Chris Gennings, Juan L. Olmo, Sebastian Ventura, Krzysztof J. Cios, and John N. Clore, “ Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records,” BioMed Research International, vol. 2014, Article ID 781670, 11 pages, 2014. This data has been prepared to analyze factors related to readmission as well as other outcomes pertaining to patients with diabetes. Source The data are submitted on behalf of the Center for Clinical and Translational Research, Virginia Commonwealth University, a recipient of NIH CTSA grant UL1 TR00058 and a recipient of the CERNER data. John Clore (jclore '@' vcu.edu), Krzysztof J. Cios (kcios '@' vcu.edu), Jon DeShazo (jpdeshazo '@' vcu.edu), and Beata Strack (strackb '@' vcu.edu). This data is a de-identified abstract of the Health Facts database (Cerner Corporation, Kansas City, MO). Data Set Information The dataset represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria: (1) It is an inpatient encounter (a hospital admission). (2) It is a diabetic encounter, that is, one during which any kind of diabetes was entered to the system as a diagnosis. (3) The length of stay was at least 1 day and at most 14 days. (4) Laboratory tests were performed during the encounter. (5) Medications were administered during the encounter. The data contains such attributes as patient number, race, gender, age, admission type, time in hospital, medical specialty of admitting physician, number of lab test performed, HbA1c test result, diagnosis, number of medication, diabetic medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization, etc. Attribute Information Detailed description of all the attributes is provided in Table 1 of the paper. Relevant Papers Beata Strack, Jonathan P. DeShazo, Chris Gennings, Juan L. Olmo, Sebastian Ventura, Krzysztof J. Cios, and John N. Clore, “Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records,” BioMed Research International, vol. 2014, Article ID 781670, 11 pages, 2014. [Web Link](https://www.hindawi.com/journals/bmri/2014/781670/)

50 features

readmitted (target)nominal3 unique values
0 missing
encounter_idnumeric101766 unique values
0 missing
patient_nbrnumeric71518 unique values
0 missing
racenominal6 unique values
0 missing
gendernominal3 unique values
0 missing
agenominal10 unique values
0 missing
weightnominal10 unique values
0 missing
admission_type_idnumeric8 unique values
0 missing
discharge_disposition_idnumeric26 unique values
0 missing
admission_source_idnumeric17 unique values
0 missing
time_in_hospitalnumeric14 unique values
0 missing
payer_codenominal18 unique values
0 missing
medical_specialtynominal73 unique values
0 missing
num_lab_proceduresnumeric118 unique values
0 missing
num_proceduresnumeric7 unique values
0 missing
num_medicationsnumeric75 unique values
0 missing
number_outpatientnumeric39 unique values
0 missing
number_emergencynumeric33 unique values
0 missing
number_inpatientnumeric21 unique values
0 missing
diag_1nominal717 unique values
0 missing
diag_2nominal749 unique values
0 missing
diag_3nominal790 unique values
0 missing
number_diagnosesnumeric16 unique values
0 missing
max_glu_serumnominal4 unique values
0 missing
A1Cresultnominal4 unique values
0 missing
metforminnominal4 unique values
0 missing
repaglinidenominal4 unique values
0 missing
nateglinidenominal4 unique values
0 missing
chlorpropamidenominal4 unique values
0 missing
glimepiridenominal4 unique values
0 missing
acetohexamidenominal2 unique values
0 missing
glipizidenominal4 unique values
0 missing
glyburidenominal4 unique values
0 missing
tolbutamidenominal2 unique values
0 missing
pioglitazonenominal4 unique values
0 missing
rosiglitazonenominal4 unique values
0 missing
acarbosenominal4 unique values
0 missing
miglitolnominal4 unique values
0 missing
troglitazonenominal2 unique values
0 missing
tolazamidenominal3 unique values
0 missing
examidenominal1 unique values
0 missing
citogliptonnominal1 unique values
0 missing
insulinnominal4 unique values
0 missing
glyburide.metforminnominal4 unique values
0 missing
glipizide.metforminnominal2 unique values
0 missing
glimepiride.pioglitazonenominal2 unique values
0 missing
metformin.rosiglitazonenominal2 unique values
0 missing
metformin.pioglitazonenominal2 unique values
0 missing
changenominal2 unique values
0 missing
diabetesMednominal2 unique values
0 missing

107 properties

101766
Number of instances (rows) of the dataset.
50
Number of attributes (columns) of the dataset.
3
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.
13
Number of numeric attributes.
37
Number of nominal attributes.
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
54864
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
1.74
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.36
Entropy of the target attribute values.
6.84
Maximum entropy among attributes.
-0.35
Minimum kurtosis among attributes of the numeric type.
4.4
Second quartile (Median) of means among attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1191.69
Maximum kurtosis among attributes of the numeric type.
0.2
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.47
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.44
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
165201645.62
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1.32
Second quartile (Median) of skewness among attributes of the numeric type.
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.04
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
18
Percentage of binary attributes.
2.99
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
790
The maximum number of distinct values among attributes of the nominal type.
-0.88
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1
Third quartile of entropy among attributes.
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
384.47
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
22.86
Maximum skewness among attributes of the numeric type.
0.93
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
13.36
Third quartile of kurtosis among attributes of the numeric type.
0.43
Average class difference between consecutive instances.
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.43
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
102640295.98
Maximum standard deviation of attributes of the numeric type.
11.16
Percentage of instances belonging to the least frequent class.
26
Percentage of numeric attributes.
29.56
Third quartile of means among attributes of the numeric type.
0.58
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.07
Average entropy of the attributes.
11357
Number of instances belonging to the least frequent class.
74
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
0.43
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
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
105.72
Mean kurtosis among attributes of the numeric type.
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of entropy among attributes.
3.09
Third quartile of skewness among attributes of the numeric type.
0.14
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.43
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
16887087.02
Mean of means among attributes of the numeric type.
0.43
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.09
First quartile of kurtosis among attributes of the numeric type.
13.9
Third quartile of standard deviation of attributes of the numeric type.
0.58
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Average mutual information between the nominal attributes and the target attribute.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.99
First quartile of means among attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.43
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
299.75
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
9
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.47
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.14
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
206.93
Standard deviation of the number of distinct values among attributes of the nominal type.
0.43
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
66.54
Average number of distinct values among the attributes of the nominal type.
0.59
First quartile of skewness among attributes of the numeric type.
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.58
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.41
Mean skewness among attributes of the numeric type.
1.36
First quartile of standard deviation of attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.43
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
0.54
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
53.91
Percentage of instances belonging to the most frequent class.
10872054.15
Mean standard deviation of attributes of the numeric type.
0.34
Second quartile (Median) of entropy among attributes.
0.47
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.14
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

17 tasks

0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: readmitted
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: readmitted
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_class_complexity - target_feature: readmitted
0 runs - estimation_procedure: 33% Holdout set - target_feature: readmitted
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: readmitted
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: readmitted
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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