Abstract
Cardiovascular disease (CVD) is a leading cause of mortality in Bangladesh, with urban populations experiencing rising risk due to lifestyle and environmental factors. This project aims to develop CardioSense, an AI-integrated system that passively collects physiological and behavioral data from smartphones and wearable devices. The system will use machine learning models to predict and monitor individual CVD risk in real time, incorporating metrics such as heart rate variability, physical activity, and sleep patterns. A pilot study will evaluate the system's accuracy, usability, and potential for scaling within urban healthcare infrastructures. By enabling low-cost, continuous monitoring, CardioSense offers a transformative approach to early detection and prevention of CVD.
Keywords: Cardiovascular disease, AI, wearable devices, smartphone health, urban health, risk prediction, digital health, Bangladesh
Public Health Relevance
Early detection of CVD through digital health tools can significantly reduce disease burden in low-resource urban settings. The project supports equitable access to preventive care and aligns with national goals for non-communicable disease (NCD) control.
Cardiovascular disease (CVD) is a leading cause of mortality in Bangladesh, with urban populations experiencing rising risk due to lifestyle and environmental factors. This project aims to develop CardioSense, an AI-integrated system that passively collects physiological and behavioral data from smartphones and wearable devices. The system will use machine learning models to predict and monitor individual CVD risk in real time, incorporating metrics such as heart rate variability, physical activity, and sleep patterns. A pilot study will evaluate the system's accuracy, usability, and potential for scaling within urban healthcare infrastructures. By enabling low-cost, continuous monitoring, CardioSense offers a transformative approach to early detection and prevention of CVD.
Keywords: Cardiovascular disease, AI, wearable devices, smartphone health, urban health, risk prediction, digital health, Bangladesh
Public Health Relevance
Early detection of CVD through digital health tools can significantly reduce disease burden in low-resource urban settings. The project supports equitable access to preventive care and aligns with national goals for non-communicable disease (NCD) control.