The United States suffers from a significant disparity in the availability of the medical resources and expertise among different regions of the country. Patients in rural areas may not have the opportunity to consult with a physician until their disease progresses to later stages, resulting in a considerable decrease in quality of life. Advances in telemedicine systems that can provide remote communication, medical data acquisition, and medical data analysis promise a significant improvement to early access to medical care and diagnoses for disadvantaged individuals.
In this thesis, we make several contributions on topics that contribute to the improvement of telemedicine systems. First, we propose several synchronization approaches for the acquisition of multimodal medical data. Second, we explore several machine learning techniques that analyze cardiovascular data and provide feedback about the patient's health to the physician. We found that the Random Forest algorithm was the most accurate in predicting heart disease in a patient.
Bachelor of Science in Compute Engineering (B.S.P.E.)
College of Engineering and Computer Science
Mostofa, Nafisa N., "Synchronization and Analysis of Multimodal Medical Data" (2021). Honors Undergraduate Theses. 988.