Integrated Health Monitoring System for Preventive Care and Doctor-Patient Interaction.

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dc.contributor.author Kaluarachchi, K.A.S.D.
dc.contributor.author Senanayaka, S.U.
dc.contributor.author Senevirathne, S.M.K.H.T.
dc.contributor.author Sandamali1, G.G.N.
dc.date.accessioned 2026-09-02T10:17:52Z
dc.date.available 2026-09-02T10:17:52Z
dc.date.issued 2026-03-04
dc.identifier.citation Kaluarachchi, K. A. S. D., Senanayaka, S. U., Senevirathne, S. M. K. H. T. & Sandamali1, G. G. N. (2026). Integrated Health Monitoring System for Preventive Care and Doctor-Patient Interaction. 23rd Academic Sessions & Vice – Chancellor’s Awards, Faculty of Engineering, University of Ruhuna, Sri Lanka. 74. en_US
dc.identifier.issn 2362-0412
dc.identifier.uri http://ir.lib.ruh.ac.lk/handle/iruor/21699
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher Faculty of Engineering , University of Ruhuna, Sri Lanka. en_US
dc.subject Doctor-Patient en_US
dc.subject Communication en_US
dc.subject Health Monitoring en_US
dc.subject Convolutional Neural Network en_US
dc.subject Preventive Healthcare en_US
dc.subject Nutrition Analysis en_US
dc.title Integrated Health Monitoring System for Preventive Care and Doctor-Patient Interaction. en_US
dc.type Article en_US


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