In higgi13425/medicaldata: Data Package for Medical Datasets

knitr::opts_chunk$set(echo = TRUE)

Source:

This data set was provided through funding from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) as the dataset "diabetes". The donor of dataset to UCI was Vincent Sigillito of Johns Hopkins.

Description:

This dataset was collected with funding from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The Pima Indian tribe located near Phoenix, Arizona (USA) has a very high rate of type 2 diabetes. This dataset includes a number of variables predictive of diabetes, and the outcome of a type 2 diabetes diagnosis within 5 years of the initial measurements. This dataset includes only females of at least 21 years of age, and of Pima Indian heritage, with at least 5 years of followup in a longitudinal study of diabetes.

The Pima Indian tribe has participated in a longitudinal diabetes study since 1965 because of its high incidence rate of diabetes. Each community resident over 5 years of age was asked to undergo a standardized examination every two years, which included an oral glucose tolerance test. Diabetes was diagnosed according to World Health Organization Criteria; that is, if the 2 hour post-load plasma glucose was at least 200 mg/dl at any survey examination or if the Indian Health Service Hospital serving the community found a glucose concentration of at least 200 mg/dl during the course of routine medical care.

Potential Analyses

Some interesting potential analyses include classifying cases as future diabetes or not future diabetes with logistic regression or forms of machine learning. Deriving variables, including the glucose to insulin ratio, and the triceps to bmi ratio, may improve prediction.

Abstract

This data set contains 768 participants at risk of diabetes who particiated in the longitudinal NIDDK study, with baseline measures and 5 year diabetes outcomes. There are several clinical variables that may be risk factors of future diabetes, including pregnancy, glucose, insulin, bmi, triceps measurement of body fat, and pedigree (family history of diabetes as a numeric score). The outcome variable is diabetes_5y, and we hope to identify predictors of future diabetes.

Study Design

Prospective Observational Cohort

A data frame with 768 observations on 9 variables, with significant missing data for some predictor variables.

Variables

pregnancy_num - number of pregnancies from 0 to 17; type: double

glucose_mg-dl - Plasma glucose concentration at 2 hours after administration of an oral glucose tolerance test in mg/deciliter, from 44 to 199, 5 missing; type: double

triceps_mm - triceps skin fold thickness in mm, a measure of subcutaneous fat, from 7 to 99, 227 missing; type: double

insulin_microiu-ml - serum insulin at 2 hours after administration of an oral glucose tolerance test in microIU per milliliter (IU is international units), from 14 to 846, 374 missing; type: double

bmi - body mass index, in kg of weight per meters of height squared, from 18.2 to 67.1, 11 missing; type: double

pedigree - a diabetes pedigree score, with points added for each additional relative with diabetes, weighted for the closeness of their genetic relation to the participant, from 0.78 to 2.42. Zero missing; type: double

age Age in years, from 21 to 81, Zero missing; type: double

diabetes_5y - Diagnosis of diabetes in the following 5 years - pos or neg, the outcome variable, 268 pos, Zero missing; type: factor

Details

Type 2 diabetes mellitus (DM) is associated with obesity and a diet high in sugars and low in vegetables. People with type 2 DM become less sensitive to insulin, so that after a glucose load, their blood glucose and insulin rise, but the glucose level does not fall as quickly as it should, leading to sustained elevations in glucose and insulin. The incidence of type 2 DM is rising in many western cultures, as increasingly unhealthy and calorie rich diets become common. A version of this dataset is available through the UCI (University of California-Irvine) Machine Learning Repository as "PimaIndiansDiabetes". This dataset was recoded with NA for zero values which were likely to be missing in the variables glucose_mg-dl, insulin_mIU-mL, dbp_mm-hg, triceps_mm, and bmi by Friedrich Leisch. The units of each predictor were added to variable names, and several variables renamed for clarity by Peter Higgins.

The primary analysis task is to classify in each participant whether diabetes developed within 5 years of data collection (diabetes_5y = pos), or the participant tested repeatedly negative for diabetes over the next 5 years (diabetes_5y = neg).

Citation(s)

These data were published across many manuscripts about gene-environment interactions affecting diabetes. An overview of the study and the long-term impact on our understanding of diabetes can be found here

higgi13425/medicaldata documentation built on Aug. 27, 2023, 1:31 a.m.