Long COVID Prediction

University Projects #Data Science#Machine Learning#Research Featured

NOTE
Click this link to view the code and original paper on GitHub.

Overview#

A healthcare analytics research project that analyzed COVID-19 patient data to predict Long COVID cases using machine learning and data mining techniques. This collaborative work resulted in three IEEE-published papers, demonstrating the ability to translate complex analysis into actionable healthcare insights.

Key Concepts#

3 IEEE publications as co-author on peer-reviewed conference papers
Mined association rules achieving >0.8 confidence scores identifying patterns between demographics and Long COVID symptoms
Identified that individuals assigned female at birth develop Long COVID at higher rates
Discovered that cough, headache, and fatigue are the most prevalent symptoms for Long COVID
Created publication-quality visualizations with Matplotlib supporting research findings
Collaborated with 4-person research team on data analysis and academic writing

My Contributions#

Performed data mining using the Apriori algorithm in Python
Preprocessed large datasets using Pandas
Create data visualizations for data mining results using Matplotlib

Publications#

“Mining Big Healthcare Data to Predict Long COVID Cases”
- IEEE International Conference on Industrial Technology (ICIT), 2023
- DOI: 10.1109/ICIT58465.2023.10143145
“A Data Science Solution for Analyzing Long COVID Cases”
- IEEE International Conference on Information Reuse and Integration (IRI), 2023
- DOI: 10.1109/IRI58017.2023.00046
“Data Analytics and Prediction of Long COVID Cases with Fuzzy Logic”
- IEEE International Conference on Fuzzy Systems (FUZZ), 2023
- DOI: 10.1109/FUZZ52849.2023.10309753