Enhancing Diabetes Management through Ensemble Models and CloudBased Big Data Analytics for Personalized Detection and Diet Planning
Keywords:
Ensemble Model, Diabetes Detection, Diet Plan Suggestion, Healthcare, Internet of Medical ThingsAbstract
Millions of people suffer from chronic diabetes. It needs constant monitoring and control to avoid problems. Technology has shifted healthcare toward data-driven disease detection, monitoring, and individualized treatment methods. Big Data analytics, especially in healthcare clouds, can analyze massive patient data to improve diabetes control. Traditional diabetes detection and diet planning use rule-based algorithms or rudimentary machine learning models. These systems may not capture complex data linkages or respond to patient health changes. They may not completely utilize cloudbased large-scale healthcare data. However, present diabetes detection and diet planning systems frequently lack the sophistication to manage patient data complexity and unpredictability. The sheer volume of healthcare data in cloud systems makes processing and extracting relevant information difficult. A more advanced approach is needed to improve diabetes care accuracy, efficiency, and customisation. Modern analytics and machine learning are needed to improve diabetes detection and provide personalized food recommendations for each patient. Therefore, this effort intends to construct a user interface and cloud model using an ensemble architecture for diabetes detection and diet planning, which will revolutionize healthcare analytics. Ensemble frameworks capture detailed data patterns better than individual models, improving diabetes prediction and diet planning. Ensemble models excel at healthcare large data clouds' dynamic and diverse nature due to their robustness and adaptability. Ensemble frameworks scale to process vast amounts of healthcare data efficiently, enabling real-time analytics and decision-making. The transformative potential to improve diabetes management precision, adaptability, and efficiency improves patient care and outcomes.
Downloads
References
P. Kaur, N. Sharma, A. Singh, and B. Gill, “CI-DPF: a cloud IoT based framework for diabetes
prediction,” in 2018 IEEE 9th annual information technology, Electronics and Mobile
Communication Conference (IEMCON), pp. 654–660, Vancouver, BC, Canada, 2018.
R. K. Barik, R. Priyadarshini, H. Dubey, V. Kumar, and K. Mankodiya, “FogLearn,” International
Journal of Fog Computing (IJFC), vol. 1, no. 1, pp. 15–34, 2018.
T. N. Gia, I. B. Dhaou, M. Ali et al., “Energy efficient fog-assisted IoT system for monitoring
diabetic patients with cardiovascular disease,” Future Generation Computer Systems, vol. 93, pp.
–211, 2019.
M. Devarajan, V. Subramaniyaswamy, V. Vijayakumar, and L. Ravi, “Fog-assisted personalized
healthcare-support system for remote patients with diabetes,” Journal of Ambient Intelligence
and Humanized Computing, vol. 10, no. 10, pp. 3747–3760, 2019.
M. Abdel-Basset, G. Manogaran, A. Gamal, and V. Chang, “A novel intelligent medical decision
support model based on soft computing and IoT,” IEEE Internet of Things Journal, vol. 7, no. 5,
pp. 4160–4170, 2019.
A. U. Haq, J. P. Li, J. Khan et al., “Intelligent machine learning approach for effective recognition
of diabetes in E-healthcare using clinical data,” Sensors, vol. 20, no. 9, p. 2649, 2020.
S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and prediction of
diabetes mellitus using soft voting classifier,” International Journal of Cognitive Computing in
Engineering, vol. 2, pp. 40–46, 2021.
G. Geetha and K. M. Prasad, “An hybrid ensemble machine learning approach to predict type 2
diabetes mellitus,” Webology, vol. 18, no. Special Issue 02, pp. 311–331, 2021.
P. G. Shynu, V. G. Menon, R. L. Kumar, S. Kadry, and Y. Nam, “Blockchain-based secure
healthcare application for diabetic-cardio disease prediction in fog computing,” IEEE Access,
vol. 9, pp. 45706–45720, 2021.
A. Singh, A. Dhillon, N. Kumar, M. S. Hossain, G. Muhammad, and M. Kumar, “eDiaPredict:
an ensemble-based framework for diabetes prediction,” ACM Transactions on Multimedia
Computing Communications and Applications, vol. 17, no. 2s, pp. 1–26, 2021.
D. S. Rajput, S. M. Basha, Q. Xin et al., “Providing diagnosis on diabetes using cloud
computing environment to the people living in rural areas of India,” Journal of ambient
intelligence and humanized Computing, vol. 13, no. 5, pp. 2829–2840, 2022.
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
