Using Machine Learning Techniques to Predict RT-PCR Results for COVID-19 Patients

Using the Machine Learning tool, WEKA, we will explore the role of Machine Learning in combatting COVID-19

Capstone Information

Acknowledgements and References

Acknowledgements

Special thanks to co-author & Project Owner Dr. Pouriyeh for guidance through this project.

Special thanks to Dr. Langer and Dr. Faravato for allowing us to use the data set they collected for their article .


Poster References

[1] Langer, M. Favarato, R. Giudici, G. Bassi, R. Garberi, F. Villa, H. Gay, A. Zeduri, S. Bragagnolo, A. Molteni et al. , “Development of machine learning models to predict rt-pcr results for severe acute respiratory syndrome coronavirus 2 (sars-cov-2) in patients with influenza-like symptoms using only basic clinical data,” Scandinavian journal o trauma, resuscitation and emergency medicine , vol. 28, no. 1, pp. 1–14, 2020.

[2] Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.


Project Description

With the COVID-19 pandemic still a threat, healthcare professionals and medical industries keep searching for better ways to mitigate the spread of COVID-19. While Machine Learning has been applied in many other domains, there is now a high demand for diagnosis systems that utilize Machine Learning techniques in the healthcare domain and in particular combating COVID-19. In this project, we explore the role of Machine Learning models in combating COVID-19, using WEKA as the main tool for analysis.

Meet the Team

Seyedamin Pouriyeh

Indya Andrews

Ming Yang

Bradley Durden

Demontae Moore

Andy Reynolds

Thomas Phillips

Mathew Shulman