diabetes.names

| .names file created by George John, October 1994
| This data seems to be the same as the irvine pima database.
| See the diabetes.irvinediff file for the differences.  They all seem
| to be formatting differences (eg 0.1 vs 0.100).
|
|1. TITLE
|	Pima Indians Diabetes Database
|
|2. USE IN STATLOG
|	2.1- Testing Mode	
|		12 Fold Cross-Validation
|
|	2.2- Special PreProcessing
|		
|	2.3- Test Results
|
|				Success Rate	TIME
|		Algorithm	Train	Test	Train	Test
|		--------------------------------------------	
|		LogDisc		78.09	77.700	31	7
|		Dipol92		?	77.600
|		Discrim		78.01	77.500	27.3	6
|		Smart		82.27	76.800	314	?
|		Radial		?	75.700
|		Itrule		?	75.500
|		BackProp	?	75.200
|		Cal5		76.8	75.000	40	1
|		Cart		77.31	74.500	61	2
|		Castle		73.97	74.200	29	4
|		QuaDisc		76.28	73.800	24	6
|		Bayes		76.07	73.800	2	1
|		C4.5		86.92	73.000	12	1
|		IndCart		92.14	72.900	18	17
|		BayTree		?	72.900
|		LVQ		?	72.800
|		Kohonen		?	72.700
|		Ac2		100	72.400	648	29
|		NewId		100	71.100	10	10
|		Cn2		98.98	71.100	38	3
|		Alloc80		71.24	69.900	115	?
|		KNN		100	67.600	1	2
|		Default		?	65.000
|		Cascade		?	0.00
|
|		
|3. SOURCES and PAST USAGE
|	3.1 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
|
|	3.2 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 "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. DATASET DESCRIPTION
|
|		NUMBER of EXAMPLES:	768
|
|		NUMBER of CLASSES:	2
|			1, 2		
|			(class value 1 is interpreted as "tested positive for
|   				diabetes")
|			Class Distribution: 
|	   			Class Value  Number of instances
|   				1            500 (65.1%)
|   				2            268 (34.9%)
|
|		NUMBER of ATTRIBUTES: 8
|	
|   			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)
|
|		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
|
|		Missing Attribute Values: None
|
|   	
|	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.
|
|CONTACTS
|	statlog-adm@ncc.up.pt
|	bob@stams.strathclyde.ac.uk
|	

1,2.
A1: continuous.
A2: continuous.
A3: continuous.
A4: continuous.
A5: continuous.
A6: continuous.
A7: continuous.
A8: continuous.