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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Original
Published: 2023-12-06
DOI: https://doi.org/10.56294/saludcyt2023556

Exploring the associations between Diabetes Mellitus and Diabetic Retinopathy: Prevention and Management by focus on Machine Learning Technique

By
Sirajudeen Ameerjohn ,
Sirajudeen Ameerjohn

VIT Bhopal University, School of Computing Science and Engineering, India

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Palaniappan Senthilnathan ,
Palaniappan Senthilnathan

Vellore Institute of Technology, School of Computer Science and Engineering, Vellore.

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Venkatachalam Ilayaraja ,
Venkatachalam Ilayaraja

Vellore Institute of Technology, School of Computer Science and Engineering, Vellore.

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Ginnela Gopichand ,
Ginnela Gopichand

Vellore Institute of Technology, School of Computer Science and Engineering, Vellore.

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Abstract

Introduction: Diabetes Mellitus, a disorder impacting insulin production and utilization, led to elevated blood sugar levels. Immune system assaults on insulin-producing pancreas cells caused Type 1 Diabetes Mellitus, while Type 2 Diabetes Mellitus affected glucose processing, predominantly in adults but also observed in children. Unmanaged diabetes resulted in varied health issues including heart disease, kidney damage, nerve impairment, and diabetic retinopathy, a major cause of adult blindness. Objective: To prevent diabetic retinopathy through glycemic control, achieved via management, lifestyle choices, screenings, treatments, education, and awareness. Machine learning techniques like transfer learning, ensemble learning, CNN-MNIST, and multiscale approaches showed promise in detection and diagnosis. Monitoring blood sugar and eye exams were vital for early retinopathy treatment. Result: DR risk is elevated in those with positive complications like nephropathy, heart disease, cerebrovascular disease, foot ulcers and HbA1C levels ≥6.8%. Retinal imaging aids diagnosis and monitoring of ocular diseases like DR, utilizing processed monochrome images for structural analysis. Method: involved observing NPDR, MPDR via eye exams, measuring glucose, visual acuity, and retinal thickness. Retinal imaging aided ocular disease diagnosis, utilizing processed images for analysis. Conclusion: Diabetes prevalence rose globally, projected to affect 800 million adults by 2050. High India rates emphasized healthcare need, especially in remote areas, addressing diabetic retinopathy and early symptom awareness.

Keywords:

Transfer Learning,
Diabetes Mellitus,
Gestational Complications,
Machine Learning,
Ensemble Learning,
CNN-MNIST,

How to Cite

1.
Ameerjohn S, Senthilnathan P, Ilayaraja V, Gopichand G. Exploring the associations between Diabetes Mellitus and Diabetic Retinopathy: Prevention and Management by focus on Machine Learning Technique. Salud, Ciencia y Tecnología [Internet]. 2023 Dec. 6 [cited 2024 Jul. 27];3:556. Available from: https://revista.saludcyt.ar/ojs/index.php/sct/article/view/556

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 3 (2023)
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