Abstract:
Diabetes, hypertension, and cholesterol disorders are some of the major chronic
diseases globally. Early diagnosis, continuous monitoring, and strong doctorpatient
communication are essential for controlling these diseases. While existing
digital health systems focus on either medical data or lifestyle monitoring, they
lack an integrated approach for preventive care. This system proposes a personalized
health monitoring system combining medical data tracking, nutritional
monitoring, and interactive doctor-patient communication. The system is built
using Node.js, React/React Native, and Firebase, enabling real-time data management.
The system has two types of users: patient and doctor. Patients can
enter or upload their medical records into the system, including details related to
blood pressure, blood sugar, lipid profile, and full blood count. They can monitor
their dietary intake manually or by uploading pictures of consumed food.
A convolutional neural network trained on 5,000 labeled food images across five
food classes was used to recognize food items and estimate nutritional parameters
such as calories, sugar, and cholesterol, achieving a validation accuracy of
approximately 90%. The estimated values are compared with recommended daily
intake limits, and warning messages are generated when thresholds are exceeded,
providing actionable insights to support healthier dietary choices. A dedicated
dashboard enables doctors to view patient medical histories and provide feedback.
The system supports active patient engagement through continuous selfmonitoring
and timely remote medical feedback. By enabling early identification
of potential health issues and remote consultations, the system reduces unnecessary
hospital visits, while the integrated real-time chat platform facilitates efficient
doctor-patient communication. Overall, the system supports personalized health
monitoring and preventive healthcare through integrated data tracking and interactive
digital communication.
