Portfolio
- Communicated with technical and non-technical stakeholders to meet the functional specifications
- Designed database in SQLite intended to allow patients and health providers to track the risk factors associated with type 2 diabetes
- Programmed database to include patient lifestyle choices and provider visit information as per functional specifications
- Worked with stakeholders on requirements, modeling, and implementation
- Implemented algorithm in Python that discovered approximate determinations for any hypothetical data set
- Github Repo
- Designed a classification problem in collaboration with a 2-person student team and domain experts at the Department of Biomedical Informatics (DBMI), VUMC
- Utilized and evaluated innovative machine learning algorithms from decision trees to neural networks in Python to classify outcomes and compare algorithm performance with traditional stroke risk prediction models; most balanced model produced an accuracy of 72.4% and a false negative rate of 59.0%
- Analyzed technical problems surrounding algorithms to troubleshoot major technical issues and optimize machine learning models while testing the best hyperparameters