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2011-09-14 16:22 by cong | Version 7 | Rating

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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
Download: # Instances: 768 / # Attributes: 9
HDF5 (64.5 KB) XML CSV ARFF LibSVM Matlab Octave
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Completeness of this item currently: 100%.

Original Data Format

libsvm

Name

diabetes_scale

Version mldata

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

Title: Pima Indians Diabetes Database
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
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.
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.
Number of Instances: 768
Number of Attributes: 8 plus class
For Each Attribute: (all numeric-valued)
Number of times pregnant
Plasma glucose concentration a 2 hours in an oral glucose tolerance test
Diastolic blood pressure (mm Hg)
Triceps skin fold thickness (mm)
2-Hour serum insulin (mu U/ml)
Body mass index (weight in kg/(height in m)^2)
Diabetes pedigree function
Age (years)
Class variable (0 or 1)
Missing Attribute Values: None
Class Distribution: (class value 1 is interpreted as "tested positive for diabetes")

Class Value Number of instances 0 500 1 268

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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Tasks defined on dataset diabetes_scale

pima binclass roc 2010-11-28 15:32
pima binclass 2010-11-28 14:44

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Acknowledgements

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

http://www.pascal-network.org/.