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http://ir.lib.seu.ac.lk/handle/123456789/6352
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DC Field | Value | Language |
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dc.contributor.author | Jayasekara, J. T. N. N. | - |
dc.contributor.author | Gunasekera, R. H. | - |
dc.contributor.author | Ravindu Hasanka, V. G. | - |
dc.contributor.author | Hasintha Kashmika, H. B. G. | - |
dc.contributor.author | Suriyaa Kumari, P. K. | - |
dc.contributor.author | Ravi Supunya Swarnakanthac, N. H. P. | - |
dc.date.accessioned | 2022-12-06T10:58:43Z | - |
dc.date.available | 2022-12-06T10:58:43Z | - |
dc.date.issued | 2022-11-15 | - |
dc.identifier.citation | 11th Annual Science Research Sessions 2022 (ASRS-2022) Proceedings on "“Scientific Engagement for Sustainable Futuristic Innovations”. 15th November 2022. Faculty of Applied Sciences, South Eastern University of Sri Lanka, Sammanthurai, Sri Lanka. pp. 21. | en_US |
dc.identifier.isbn | 978-624-5736-60-7 | - |
dc.identifier.uri | http://ir.lib.seu.ac.lk/handle/123456789/6352 | - |
dc.description.abstract | Especially today's society tends to use new technological devices instead of relying on on document materials in daily life. While there are diabetes-related apps that more accurately predict users' prediabetes or diabetes type 2 using machine learning approaches, predicting health risks by analyzing glucose monitoring data, recommend meal and exercise plans, and use a non-invasive approach to measure and monitor blood glucose level, heart rate, and blood oxygen level, and over Wi-Fi using NodeMCU makes the proposed DiaBeta application unique among diabetes applications. Other secondary functions such as digital logbook, reminders, lifestyle-based meal recommendations, medical guidelines, and efforts such as glucose monitoring data can be easily performed with a smartphone. DiaBeta is a life-saving app that can be used by anyone around the world to get a more accurate and personalized meal plan. DiaBeta offers a precise, clinical, validated, and standardized solution for diabetes patients. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Faculty of Applied Sciences, South Eastern University of Sri Lanka, Sammanthurai. | en_US |
dc.subject | Non-Invasive | en_US |
dc.subject | Diabetes Prediction | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | NodeMCU | en_US |
dc.subject | LoT | en_US |
dc.title | Intelligent, secured smart app for complete diabetes lifestyle management –“Diabeta” | en_US |
dc.type | Article | en_US |
Appears in Collections: | 11th Annual Science Research Session - FAS |
Files in This Item:
File | Description | Size | Format | |
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Computer Sc 3.pdf | 420.01 kB | Adobe PDF | View/Open |
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