GC-258 Heart Disease Prediction using Machine Learning

Research has shown that the early detection of Heart Disease is critical to treating and understanding the causes. Through the use of advanced machine learning models and com- prehensive data sets collected on patients of varying backgrounds and health statuses, this research shows the listed correlations between attributes of data points and positive identification of the disease. This research uses 1026 unique records and 14 attributes including the classifier of Heart Disease. These attributes range from simple (cholesterol level) to more complex and subjective (chest pain type) but each attribute presents an opportunity to improve each of the analyzed models significantly. Index Terms—WEKA, Machine Learning, Health Dat

UC-36 Using Machine Learning Techniques to Predict RT-PCR Results for COVID-19 Patients.

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.Advisors(s): Dr. Ming Yang - IT 4983 Capstone Professor Dr. Seyedamin Pouriyeh - Project OwnerTopic(s): Data/Data AnalyticsIT 498