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Uni Köln
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Mosler > Prof. Mosler > Datenportal
Datenportal des Lehrstuhls für Statistik und Ökonometrie
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Pima Indians Diabetes data
The data set (and description) can be downloaded here:
http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data
Description:
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: Yes
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
Citation Request:
Please refer to the repository http://archive.ics.uci.edu/ml (see citation policy).
See also Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
Descriptive statistics:
Dataset= diabetes : n= 768 , d= 8
Class1: n= 268
Covariance matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 13.9969 -6.5233 10.2086 -5.2363 -40.7637 -4.3224 -0.0964 18.2600
[2,] -6.5233 1020.1395 47.1577 21.2422 1157.7746 11.6957 0.3149 34.5295
[3,] 10.2086 47.1577 461.8980 85.5870 266.3737 20.9090 0.2763 61.9219
[4,] -5.2363 21.2422 85.5870 312.5722 1119.4727 40.0705 1.8031 -17.8425
[5,] -40.7637 1157.7746 266.3737 1119.4727 19234.6733 55.5141 5.2450 36.4231
[6,] -4.3224 11.6957 20.9090 40.0705 55.5141 52.7507 0.3699 -14.9774
[7,] -0.0964 0.3149 0.2763 1.8031 5.2450 0.3699 0.1386 -0.3599
[8,] 18.2600 34.5295 61.9219 -17.8425 36.4231 -14.9774 -0.3599 120.3026
Correlation matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1.0000 -0.0546 0.1270 -0.0792 -0.0786 -0.1591 -0.0692 0.4450
[2,] -0.0546 1.0000 0.0687 0.0376 0.2614 0.0504 0.0265 0.0986
[3,] 0.1270 0.0687 1.0000 0.2252 0.0894 0.1340 0.0345 0.2627
[4,] -0.0792 0.0376 0.2252 1.0000 0.4566 0.3121 0.2739 -0.0920
[5,] -0.0786 0.2614 0.0894 0.4566 1.0000 0.0551 0.1016 0.0239
[6,] -0.1591 0.0504 0.1340 0.3121 0.0551 1.0000 0.1368 -0.1880
[7,] -0.0692 0.0265 0.0345 0.2739 0.1016 0.1368 1.0000 -0.0881
[8,] 0.4450 0.0986 0.2627 -0.0920 0.0239 -0.1880 -0.0881 1.0000
Median: 4.8835 137.5659 71.6751 20.1639 59.7598 34.7332 0.5374 36.6408
Mean: 4.8657 141.2575 70.8246 22.1642 100.3358 35.1425 0.5505 37.0672
MCD-estimated:
MDC-0.975-Mean: 5.2391 137.5507 67.7609 11.9928 0 34.5442 0.4798 38.1304
MDC-0.750-Mean: 5.2391 137.5507 67.7609 11.9928 0 34.5442 0.4798 38.1304
MDC-0.500-Mean: 5.2391 137.5507 67.7609 11.9928 0 34.5442 0.4798 38.1304
Class2: n= 500
Covariance matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 9.1034 7.7835 7.2537 -5.3165 -39.3708 0.3827 -0.0721 20.1637
[2,] 7.7835 683.3623 91.0358 6.2337 912.2022 26.4845 0.7470 69.5469
[3,] 7.2537 91.0358 326.2747 50.3145 133.2688 50.4463 0.1474 45.2475
[4,] -5.3165 6.2337 50.3145 221.7105 607.6674 50.2211 0.4239 -28.4551
[5,] -39.3708 912.2022 133.2688 607.6674 9774.3454 193.2592 6.7236 -172.1448
[6,] 0.3827 26.4845 50.4463 50.2211 193.2592 59.1339 0.1625 3.2363
[7,] -0.0721 0.7470 0.1474 0.4239 6.7236 0.1625 0.0895 0.1454
[8,] 20.1637 69.5469 45.2475 -28.4551 -172.1448 3.2363 0.1454 136.1342
Correlation matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1.0000 0.0987 0.1331 -0.1183 -0.1320 0.0165 -0.0800 0.5728
[2,] 0.0987 1.0000 0.1928 0.0160 0.3530 0.1317 0.0955 0.2280
[3,] 0.1331 0.1928 1.0000 0.1871 0.0746 0.3632 0.0273 0.2147
[4,] -0.1183 0.0160 0.1871 1.0000 0.4128 0.4386 0.0952 -0.1638
[5,] -0.1320 0.3530 0.0746 0.4128 1.0000 0.2542 0.2274 -0.1492
[6,] 0.0165 0.1317 0.3632 0.4386 0.2542 1.0000 0.0707 0.0361
[7,] -0.0800 0.0955 0.0273 0.0952 0.2274 0.0707 1.0000 0.0417
[8,] 0.5728 0.2280 0.2147 -0.1638 -0.1492 0.0361 0.0417 1.0000
Median: 3.1352 104.3811 68.3522 19.2381 45.9682 29.5276 0.4185 30.0064
Mean: 3.298 109.98 68.184 19.664 68.792 30.3042 0.4297 31.19
MCD-estimated:
MDC-0.975-Mean: 2.1938 106.7656 68.4969 21.2531 66.4313 30.375 0.3921 25.3156
MDC-0.750-Mean: 2.1759 106.9198 68.6636 21.4444 67.9506 30.3611 0.395 25.4198
MDC-0.500-Mean: 2.1651 107.1371 68.6324 21.2804 67.2523 30.3648 0.3941 25.2804
Measures:
Mah.Dist: 1.3823
Mah.Dist-MCD-0.975: 2.3485
Mah.Dist-MCD-0.750: 2.3532
Mah.Dist-MCD-0.500: 2.3541
Zuletzt geändert am 17.02.2013
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