Level of Education of Students Involved
Graduate
Faculty Sponsor
Hugh Gong
College
College of Arts & Sciences (CAS)
Discipline(s)
Mathematics & Statistics
Presentation Type
Poster Presentation
Symposium Date
Spring 4-24-2025
Abstract
Chronic diseases like cardiovascular diseases and diabetes account for a considerable proportion of worldwide mortality rates, and early detection and prevention are pivotal. The purpose of this project is to create a model that predicts the onset of chronic disease using available physiological data from wearable devices. The system aims to apply machine learning and deep learning methodologies to generate early warning alerts for the patient and their caretakers, as well as personalized preventive care based on continuous health monitoring of the patient. Wearable technologies, despite the current battery life and device bulkiness limitations, represent a promising avenue for collecting and analyzing health data at scale(Yanagita, M. (2023)).
The project also includes a web platform that will promote a community where patients, doctors, and other healthcare professionals can share knowledge and provide and receive support. It would be a central site for tracking risk factors, providing health alerts, and enabling early treatment. In the longer run, this two-phase program is intended to transform chronic disease management by providing users with accurate prediction, to help to reduce dependence on expensive traditional tests and placing the power in the users' hands to enable them take responsibility of their health(Yanagita, M. (2023)).
Keywords: Chronic disease prediction, wearable health devices, early detection, preventive healthcare, machine learning, deep learning, real-time monitoring, health analytics, digital health platform, personalized medicine, health forecasting, cardiovascular disease, diabetes, health data integration, patient engagement.
References:
[1] Sato, Y., Silina, K., van den Broek, M., Hirahara, K., & Yanagita, M. (2023). The roles of tertiary lymphoid structures in chronic diseases. Nature Reviews Nephrology, 19(8), 525-537. [2] Jomova, K., Raptova, R., Alomar, S. Y., Alwasel, S. H., Nepovimova, E., Kuca, K., & Valko, M. (2023). Reactive oxygen species, toxicity, oxidative stress, and antioxidants: Chronic diseases and aging. Archives of toxicology, 97(10), 2499-2574.
Recommended Citation
Shaik, Irshad, "Predicting Chronic Disease Onset Using Wearable Data" (2025). Symposium on Undergraduate Research and Creative Expression (SOURCE). 1448.
https://scholar.valpo.edu/cus/1448