DEVELOPING MACHINE LEARNING APPLICATION FOR EARLY CARDIOVASCULAR DISEASE (CVD) RISK DETECTION IN FIJI: A DESIGN SCIENCE APPROACH
Shahil SHARMA
shahil.sharma@fnu.ac.fjFiji National University (Fiji)
Rajnesh LAL
Fiji National University (Fiji)
https://orcid.org/0000-0002-3034-9751
Bimal KUMAR
Fiji National University (Fiji)
https://orcid.org/0000-0003-3622-7541
Abstract
CVD (cardiovascular disease) has become a significant contributor to premature deaths for many years in Fiji. CVD's late detection also significantly impacts annual deaths and casualties. Currently, Fiji lacks diagnosis tools that would enable people to know their risk levels. In this paper, a machine learning mobile application was developed that can be easily accessible to the local population for early prediction of CVD risk. The design science approach was used to guide the development of the application. The design process involved identifying the problem and motivation, setting objectives, creating a machine-learning mobile application for medical record analysis, demonstrating the application to selected participants, evaluating its usability and the machine-learning model's performance, and communicating the findings. The results revealed that the application proposed in this paper is an effective tool for CVD prediction in Fiji.
Keywords:
Mobile Health, Machine Learning, Design Science, Cardiovascular DiseaseReferences
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