View datasets-UCI diabetes (public)

2010-11-06 09:57 by mldata | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
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Summary

(No information yet)

License
unknown (from Weka repository)
Dependencies
Tags
arff slurped Weka
Attribute Types
Integer,Floating Point,String
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# Instances: 768 / # Attributes: 9
HDF5 (83.8 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
arff
Name
pima_diabetes
Version mldata
0
Comment
  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

Relabeled values in attribute 'class' From: 0 To: tested_negative
From: 1 To: tested_positive

Names
preg,plas,pres,skin,insu,mass,pedi,age,class,
Types
  1. numeric
  2. numeric
  3. numeric
  4. numeric
  5. numeric
  6. numeric
  7. numeric
  8. numeric
  9. nominal:tested_negative,tested_positive
Data (first 10 data points)
    preg plas pres skin insu mass pedi age class
    6 148 72 35 0 33.6 0.627 50 test...
    1 85 66 29 0 26.6 0.351 31 test...
    8 183 64 0 0 23.3 0.672 32 test...
    1 89 66 23 94 28.1 0.167 21 test...
    0 137 40 35 168 43.1 2.288 33 test...
    5 116 74 0 0 25.6 0.201 30 test...
    3 78 50 32 88 31.0 0.248 26 test...
    10 115 0 0 0 35.3 0.134 29 test...
    2 197 70 45 543 30.5 0.158 53 test...
    8 125 96 0 0 0.0 0.232 54 test...
    ... ... ... ... ... ... ... ... ...
Description

A jarfile containing 37 classification problems, originally obtained from the UCI repository (datasets-UCI.jar, 1,190,961 Bytes).

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Publications
    Data Source
    http://www.ics.uci.edu/~mlearn/MLRepository.html
    Measurement Details
    Usage Scenario
    revision 1
    by mldata on 2010-11-06 09:57

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