DIABETES DIAGNOSIS REVOLUTIONIZED PREDICTIVE POWER OF CONVOLUTIONAL NEURAL NETWORK (CNN)

Registration ID: IJNRD_215941

Published ID: IJNRD2403339

DOI: Click Here to Get

Thadeus Cruz Govindapillai , Abishek A

CNN, Diabetes, Healthcare, Prediction, Data

Diabetes mellitus is a major global health concern. It is a chronic metabolic illness marked by increased blood sugar levels. Effective diabetes management and the reduction of related complications depend on early detection and action. Convolutional Neural Networks (CNNs), in particular, are machine learning algorithms that have demonstrated potential in predictive modeling for healthcare applications in recent years. In this work, a thorough review of the use of CNNs for diabetes risk prediction using medical imaging data—such as retinal images—is presented. We go over the design of CNNs, the reasoning behind using them, and how these models are trained and assessed. We also discuss future avenues for research and clinical use, as well as possible advantages and disadvantages of CNN-based diabetic prediction. The utilization of CNNs for diabetes prediction presents significant opportunities to enhance patient outcomes in the management of diabetes by promoting early intervention, increasing diagnostic accuracy, and facilitating treatment.

Published Paper   E-Certificate

Thadeus Cruz Govindapillai & Abishek A (March-2024). DIABETES DIAGNOSIS REVOLUTIONIZED PREDICTIVE POWER OF CONVOLUTIONAL NEURAL NETWORK (CNN). INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), d287-d290. https://ijnrd.org/papers/IJNRD2403339.pdf

Volume 9 Issue 3, March-2024

Pages : d287-d290

Paper Reg. ID: IJNRD_215941

Published Paper Id: IJNRD2403339

Downloads: 000121983

Research Area: Engineering

Country: Coimbatore, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403339.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403339

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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