Transforming Diabetes Care: Leveraging Ensemble Machine Learning and Cloud Analytics for Personalized Diet Recommendations

Authors

  • Dr. Arun Elias, Chakka Balasruthi Author

DOI:

https://doi.org/10.48047/

Keywords:

Patient data, healthcare data, machine learning, ensemble framework, diabetes management, personalized diet plans, and healthcare clouds.

Abstract

Millions of individuals across the world suffer with diabetes, a chronic medical condition. To avoid difficulties, it has to be managed and monitored continuously. Healthcare is experiencing a paradigm change as a result of technological advancements, utilizing data-driven techniques to improve illness identification, management, and customized treatment options. Big Data analytics offers the chance to examine enormous volumes of patient data and produce insightful findings for improved diabetic care, especially when applied to healthcare clouds. Traditionally, rule-based algorithms or basic machine learning models have been used in diabetes diagnosis and diet planning.

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References

T. M. Fernández-Caramés and P. Fraga-Lamas, “Design of a fog computing, blockchain and IoT based continuous glucose monitoring system for crowdsourcing mHealth,” Multidisciplinary Digital Publishing Institute Proceedings, vol. 4, no. 1, p. 37, 2018.

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.

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Published

2022-08-01

Issue

Section

Articles

How to Cite

Dr. Arun Elias, Chakka Balasruthi. (2022). Transforming Diabetes Care: Leveraging Ensemble Machine Learning and Cloud Analytics for Personalized Diet Recommendations . History of Medicine, 8(2), 638-646. https://doi.org/10.48047/