Diabetes-Data-Set

OpenML dataset with id 43384

Author name not available (Why is that?)

Full work available at URL: https://api.openml.org/data/v1/download/22102209/Diabetes-Data-Set.arff

Upload date: 23 March 2022

Dataset Characteristics

Number of classes: 0
Number of features: 9 (numeric: 9, symbolic: 0 and in total binary: 0 )
Number of instances: 768
Number of instances with missing values: 0
Number of missing values: 0

Description

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)

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.

This page was built for dataset: Diabetes-Data-Set