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Summary

PIMA indian diabetes data (scaled to [-1,1])

License
unknown (from LibSVMTools repository)
Dependencies
Tags
libsvm LibSVMTools slurped
Attribute Types
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# Instances: 768 / # Attributes: 9
HDF5 (64.5 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
libsvm
Name
diabetes_scale
Version mldata
0
Comment

LibSVM

Names
Data (first 10 data points)
    -1.0 -0.29... 0.48... 0.18... -0.29... -1.0 0.00... -0.53... -0.03...
    1.0 -0.88... -0.14... 0.08... -0.41... -1.0 -0.20... -0.76... -0.66...
    -1.0 -0.05... 0.83... 0.04... -1.0 -1.0 -0.30... -0.49... -0.63...
    1.0 -0.88... -0.10... 0.08... -0.53... -0.77... -0.16... -0.92... -1.0
    -1.0 -1.0 0.37... -0.34... -0.29... -0.60... 0.28... 0.88... -0.6
    1.0 -0.41... 0.16... 0.21... -1.0 -1.0 -0.23... -0.89... -0.7
    -1.0 -0.64... -0.21... -0.18... -0.35... -0.79... -0.07... -0.85... -0.83...
    1.0 0.17... 0.15... -1.0 -1.0 -1.0 0.05... -0.95... -0.73...
    -1.0 -0.76... 0.97... 0.14... -0.09... 0.28... -0.09... -0.93... 0.06...
    -1.0 -0.05... 0.25... 0.57... -1.0 -1.0 -1.0 -0.86... 0.1
    ... ... ... ... ... ... ... ... ...
Description

This data was originally automatically copied from: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/diabetes_scale and is originally from http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes

There are various versions of this data on this repository:

URLs
http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
Publications
    Data Source
    http://www.ics.uci.edu/~mlearn/MLRepository.html UCI / Pima Indians Diabetes
    Measurement Details

    The README from UCI: http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.names

    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)

    8. Number of times pregnant

    9. Plasma glucose concentration a 2 hours in an oral glucose tolerance test

    10. Diastolic blood pressure (mm Hg)

    11. Triceps skin fold thickness (mm)

    12. 2-Hour serum insulin (mu U/ml)

    13. Body mass index (weight in kg/(height in m)^2)

    14. Diabetes pedigree function

    15. Age (years)

    16. Class variable (0 or 1)

    17. Missing Attribute Values: None

    18. Class Distribution: (class value 1 is interpreted as "tested positive for diabetes")

    Class Value Number of instances 0 500 1 268

    1. 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

    Usage Scenario

    One of the commonly used datasets for testing binary classification algorithms.

    revision 1
    by mldata on 2010-11-01 11:37
    revision 2
    by cong on 2010-11-26 15:03
    revision 3
    by sonne on 2010-11-27 00:11
    revision 4
    by sonne on 2010-11-27 00:11
    revision 5
    by sonne on 2010-11-27 00:12
    revision 6
    by sonne on 2010-11-27 00:18
    revision 7
    by cong on 2011-09-14 16:22

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    Acknowledgements

    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
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