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training_data_source.txt

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+--Training Data Set Information--
+Sourced from https://www.kaggle.com/datasets/mathchi/diabetes-data-set?resource=download
+About Dataset
+Context
+This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes.
+
+Content
+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.
+
+Pregnancies: Number of times pregnant
+Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
+BloodPressure: Diastolic blood pressure (mm Hg)
+SkinThickness: Triceps skin fold thickness (mm)
+Insulin: 2-Hour serum insulin (mu U/ml)
+BMI: Body mass index (weight in kg/(height in m)^2)
+DiabetesPedigreeFunction: Diabetes pedigree function
+Age: Age (years)
+Outcome: Class variable (0 or 1)
+Sources:
+(a) Original owners: National Institute of Diabetes and Digestive and
+Kidney Diseases
+(b) Donor of database: Vincent Sigillito ([email protected])
+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: Yes
+Class Distribution: (class value 1 is interpreted as "tested positive for
+diabetes")