As part of my undergraduate coursework, I, with a group, completed a final project for Data Mining where we built predictive models
aimed at classifying metabolic syndrome’s presence. We tested various models, including XGBoost, Logistic Regression, Random Forests,
and Neural Networks. We found a high classification rate, with our neural network performing best at a 91.4% accuracy and
a 0.91 F-1 Score. This project helped further establish a standard for what factors can lead to the syndrome and how it can be classified.
All of our code can be found here Metabolic Syndrome Project Code. We also published
a final report to explain our full process, which can be found here Metabolic Syndrome Paper.