Code for analyses in "Obesity and risk of female reproductive disorders: A Mendelian Randomisation Study"

Code to reproduce analysis and figures for 'Genetic mapping of etiologic brain cell types for obesity' (Timshel, eLife 2020)

No description provided.

🍎 A Reproducible Pipeline for Processing SISVAN Microdata on Nutritional Status Monitoring in Brazil

ObMetrics is a Shiny app developed to facilitate the calculation of outcomes related to Metabolic Syndrome in pediatric populations. This repository contains documentation and licensing details for the application, which aims to provide a user-friendly interface for healthcare professionals and researchers.

Analysis of obesity levels using MCA with two approaches for quantitative variables.

This notebook presents a concise analysis for predicting obesity risk using machine learning models like Random Forest and XGBoost. Focused on identifying key factors influencing obesity through exploratory data analysis (EDA) and predictive modeling, the notebook offers insights into effective prevention strategies.

OCS (BP): Examine global patterns of obesity across rural and urban regions

Codes for the statistical analysis that investigates the impact of high-fat diet on gut microbiome and serotonergic gene expression in the raphe nuclei.

Estimation of Obesity Levels

Python & R scripts collection for AdipoAtlas project

Repository to preview, describe, and link to multiple health-related Tableau dashboards.

📓 Exploring potential associations between childhood undernutrition and the Standardized Precipitation Evapotranspiration Index (SPEI) in Brazilian municipalities (2008–2019)

Predicting a Person's Obesity Level Using Decision Tree, Naive Bayes, and KNN Algorithms

CHAMP data wrangling codes: cleaning, reshaping and quantitive analysis of child measurement data

Conducted research and developed a system under Dr Booma Poolan Marikannan on provisional analysis for obesity issues using numerous data mining techniques by using a past medical dataset from the Kaggle.  Executed the project using tools such as PyCaret, Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, and Pickle, and evaluated the classification models to classify obesity based on the value of BMI by using Classification Report and Confusion Matrix. Achievement - Implemented supervised machine learning techniques, such as Quadratic Discriminant Analysis, K-Nearest Neighbour, and Random Forest, with an accuracy of 91% to forecast customers' profitability based on consumer products.

Some Awsome Things

Analysis of Spatial and Temporal Data Course Final Project - Obesity Classification

Classification of Obesity Status in Indonesia Using XGBoost & ADASYN-N Method

[In Production] Adaptation of Nathaniel Daw's Two-Step Sequential Learning Task. Designed for a study of reward prediction for food with college undergraduates.

Use of OLS method, Linear Regression, K-means, Agglomerative Hierarchical, DBSCAN, Decision Tree, Random Forest, Logistic Regression, Support Vector Classifier, K-nearest neighbors, and Naive Bayes algorithms in the case study to estimate obesity levels.

This repository contains the required code to reproduce the results reported on our paper entitled: Explaining the widening distribution of Body Mass Index: A decomposition analysis of trends for England, 2002/04-2012/14

Data analysis by Yuchang Bao, Yulin Huang, He Yang

Obesity Dataset

This repository contains the documentation for reproducibility of the study "Preoperative atelectasis in patients with obesity undergoing bariatric surgery: a cross-sectional study".

Using D3, this repository takes the data from the US Census Bureau's 2014 ACS 1-year estimates and creates animated visualizations from it.

Android app that predicts chronic disease risk such as diabetes, cancer, obesity, cardiovascular diseases based on user health data, written in kotlin and jetpack compose. Virtual University Final Year Project.

This repository demonstrates the usage of a Random Forest Model to to determine risk factors that lead to obesity.

Classify Indonesian Obesity Status using ADASYN-N and Random Forest algorithm

Unveiling adipose populations linked to metabolic health in obesity