Now you can request additional data and/or customized columns!
Try It Now!Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
3 | 132kB | arff csv zip | 5 years ago | 5 years ago | Open Data Commons Public Domain Dedication and License |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
diabetes_arff | 37kB | arff (37kB) | ||
diabetes | 34kB | csv (34kB) , json (105kB) | ||
diabetes_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 46kB | zip (46kB) |
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This is a preview version. There might be more data in the original version.
Signup to Premium Service for additional or customised data - Get Started
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
preg | 1 | number (default) | |
plas | 2 | number (default) | |
pres | 3 | number (default) | |
skin | 4 | number (default) | |
insu | 5 | number (default) | |
mass | 6 | number (default) | |
pedi | 7 | number (default) | |
age | 8 | number (default) | |
class | 9 | string (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/machine-learning/diabetes
data info machine-learning/diabetes
tree machine-learning/diabetes
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/diabetes/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/machine-learning/diabetes/r/0.arff
curl -L https://datahub.io/machine-learning/diabetes/r/1.csv
curl -L https://datahub.io/machine-learning/diabetes/r/2.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/machine-learning/diabetes/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
Note: You might need to run the script with root permissions if you are running on Linux machine
Install the Frictionless Data data package library and the pandas itself:
pip install datapackage
pip install pandas
Now you can use the datapackage in the Pandas:
import datapackage
import pandas as pd
data_url = 'https://datahub.io/machine-learning/diabetes/datapackage.json'
# to load Data Package into storage
package = datapackage.Package(data_url)
# to load only tabular data
resources = package.resources
for resource in resources:
if resource.tabular:
data = pd.read_csv(resource.descriptor['path'])
print (data)
For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):
pip install datapackage
To get Data Package into your Python environment, run following code:
from datapackage import Package
package = Package('https://datahub.io/machine-learning/diabetes/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
If you are using JavaScript, please, follow instructions below:
Install data.js
module using npm
:
$ npm install data.js
Once the package is installed, use the following code snippet:
const {Dataset} = require('data.js')
const path = 'https://datahub.io/machine-learning/diabetes/datapackage.json'
// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
const dataset = await Dataset.load(path)
// get list of all resources:
for (const id in dataset.resources) {
console.log(dataset.resources[id]._descriptor.name)
}
// get all tabular data(if exists any)
for (const id in dataset.resources) {
if (dataset.resources[id]._descriptor.format === "csv") {
const file = dataset.resources[id]
// Get a raw stream
const stream = await file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()
The resources for this dataset can be found at https://www.openml.org/d/37
Author: Vincent Sigillito
Source: Obtained from UCI
Please cite: UCI citation policy
Title: Pima Indians Diabetes Database
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 © Date received: 9 May 1990
Past Usage:
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)
Missing Attribute Values: None
Class Distribution: (class value 1 is interpreted as “tested positive for diabetes”)
Class Value Number of instances 0 500 1 268
Brief statistical analysis:
Attribute number: Mean: Standard Deviation:
3.8 3.4
120.9 32.0
69.1 19.4
20.5 16.0
79.8 115.2
32.0 7.9
0.5 0.3
33.2 11.8
Relabeled values in attribute ‘class’
From: 0 To: tested_negative
From: 1 To: tested_positive
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Workflow integration (e.g. Python packages, NPM packages)
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