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)

🍎 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.

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

Python & R scripts collection for AdipoAtlas project

Estimation of Obesity Levels

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

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

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

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.

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

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 classificat…

[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.

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