| --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") |