There are 4 repositories under nhanes topic.
R Package for Calculating Healthy Eating Index-2020 (HEI2020), Alternative Healthy Eating Index (AHEI), Dietary Approaches to Stop Hypertension (DASH), Mediterranean Diet (MED), Dietary Inflammation Index (DII), and Planetary Health Diet Index from the EAT-Lancet Commission (PHDI) for the NHANES, ASA24, DHQ, and other dietary assessments
R package for accessing and analyzing CDC NHANES data
This repository should help people that would like to code in R and work with the National Health and Nutrition Examination Survey (NHANES). Some topics corved are SQL , logistic regression.... etc
R-script for the publication Testosterone and specific symptoms of depression: Evidence from NHANES 2011–2016, https://doi.org/10.1016/j.cpnec.2021.100044
Coursework | A Survey-weighted Exploratory Analysis of NHANES data
Process Accelerometer Data from NHANES 2003-2006
Survey Data: Design and Examples
The NHANES Data 'API' is a Python tool that simplifies access to the National Health and Nutrition Examination Survey (NHANES) dataset. This project provides an easy-to-use API to retrieve NHANES data, helping researchers, data scientists, health professionals, and other stakeholders access these valuable datasets.
Example code for paper: "Development of a national database for dietary glycemic index and load for nutritional epidemiologic studies in the United States"
Code for the paper "Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases". Accepted by BMC Medical Informatics and Decision Making, 2021
drake project with starter code to download and clean continuous NHANES data from 1999 - 2018.
This is a R online textbook for those who are not familiar with data wrangling. For providing some practical introduction to data wrangling, NHANES datasets will be used as examples in this tutorial. Target audience is those interested in health data analysis, but these data wrangling skills are easily transferable to other fields. General understanding of a syntax based program is required as pre-requisite. For any comments regarding this document, reach out to Ehsan Karim http://ehsank.com/
This repository contains R programs for the article, “Varying-coefficient regression analysis for pooled biomonitoring,” by Dewei Wang, Xichen Mou, and Yan Liu. This article has been submitted for publication.
Survey Data Analysis using R
Data from Continuous NHANES (Years 1999-2016)
The most common discrete and continuous distributions, showing how they find use in decision and estimation problems, and constructs computer algorithms for generating observations from the various distributions. and applications, and lastly the most important concept is covered is entropy
Predictive model flagging patients who are likely to be hospitalized over the next 12 months
This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.
This analysis leverages publicly available NHANES data from the CDC to investigate insulin resistance and diabetes at the population level to determine whether public health could be improved with a more proactive approach to testing. đź’‰
An R Shiny app analyzing NHANES depression data with interactive visualizations, dimensionality reduction techniques, and predictive modeling.
R PROJECT-CDC NHANES DIABETES MELLITUS DATA ANALYSIS
NHANES 2017-2022 data was used to complete four analyses exploring factors impacting HDL cholesterol in females ages 30 - 55.
Personal research and senior capstone about nutrition using NHANES
PLAY AROUND DATA!!!
NHANES 2017-2022 data was used to build and compare two linear regression models predicting HDL cholesterol in females ages 30 - 55.
This repository contains my analysis and documentation for the NHANES 2021-2023 data in performing basic inferential statistics using Python in Google Colab. This includes exploring relationships and differences in health metrics and demographic variables, utilizing the skills learned in class to answer key questions about the dataset.