In this repository, we explore the use of BRFSS dataset for descriptive analysis.

According to the CDC, heart disease is one of the leading causes of death for people of most races in the US. Our ML project leads to a better understanding of how we can predict heart disease.

Primeiro projeto apresentado na disciplina de Inteligência Computacional em Saúde utilizando a base de dados de indicadores de saúde para tarefa de classificação de indivíduos com diabetes.

Predictors of Smoking Cessation in the U.S.: A Machine Learning Analysis Revealing the Socioeconomic Paradox in BRFSS Data (2018-2023)

Machine learning product for Diabetes prediction utilizing the BRFSS 2015 dataset. Implements a pre-trained Random Forest classifier with SHAP interpretability.

Analysis of 2023 BRFSS data exploring the relationship between insurance status, flu shot uptake, and preventive care access. Includes data cleaning, EDA, logistic regression, and visualizations using R (tidyverse, caret, broom). Data from CDC BRFSS 2023.

Predicting self-reported health in seniors who participated in the Behavioral Risk Factor Surveillance System (CRFSS) 2015 Survey.

Final Project for Coursera [Introduction to probability and Data with R] by Duke University

SQL database and analysis of CDC BRFSS Alzheimer’s & Healthy Aging data examining demographic and lifestyle factors associated with cognitive decline using SQL views, procedures, and functions.

Possible projects on Behavioral Machine Learning

State-Level Risk Factor Analysis of Mental Health Wellbeing

SYDE780-w2025 (Big Data Analysis: Health & BME): Course featuring a project on studying demographic, socioeconomic, and lifestyle patterns in cardiovascular disease based on the 2022 BRFSS dataset

Using the 2023 BRFSS Survey Data to explore the relationship between reported exercise habits and reported mental health

This project aims to compare traditional Machine Learning methods for tabular data classification, such as Ensemble methods, Decision Trees, and Naive Bayes, with NLP classification methods like Multinomial Naive Bayes, RNNs, and Transformers. We are utilizing survey data from the CDC via the Behavioral Risk Factor Surveillance System (BRFSS)