Diabetes Dataset

About this Dataset

Number of Instances: 768 Number of Attributes: 8 plus class 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)

Missing Attribute Values: Yes Class Distribution: (class value 1 is interpreted as "tested positive for diabetes")

The 'DiabetesPedigreeFunction' is a function that scores the probability of diabetes based on family history, with a realistic range of 0.08 to 2.42. Age has a realistic range from 21 to 81.

Data card

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
• (d) https://www.kaggle.com/datasets/mathchi/diabetes-data-set

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.
2. 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.
3. 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.