Commit Inicial

data/tanveer/pima/pima-indians-diabetes.names

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+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
+