Final project for CSCE 421 - Machine Learning during the Spring 2023 semester at Texas A&M University. If you're currently enrolled in this course, kindly DO NOT LOOK AT THIS REPOSITORY.
For my CSCE 421 - Machine Learning final, I contributed to a project that leverages machine learning for mortality prediction in healthcare. The goal was to build a model, using data such as age and admission weight, to anticipate a patient's mortality risk. The model's effectiveness was assessed by the ROC-AUC score. Using the Philips eICU dataset, with over 700 thousand health-related datapoints, we designed a predictive model for ICU patients' discharge status. This involved diligent data preprocessing, model training, and hyperparameter tuning. Our work promised to aid healthcare providers in recognizing high-risk patients, optimizing patient outcomes and decision-making processes.
For more information, check CSCE421_Final_Project.pdf or my website.