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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 120
Article Submitted : 24205
Article Published : 9239
Total Authors : 24597
Total Reviewer : 847
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| Paper Title: | OcuCardio:Retinal Based Cardiovascular Risk Prediction System |
| Authors Name: | M.K.V.Anvesh , Sruthi Majhi , Sura Harisha , Tellam Kusuma Ganga Sneha , Thatipudi Poojitha Sri Rani |
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IJRTI_211660
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| Published Paper Id: | IJRTI2604204 |
| Published In: | Volume 11 Issue 4, April-2026 |
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| Abstract: | Cardio Vascular Disease (CVD) is the leading cause of mortality among individuals with Type 2 Diabetes Mellitus (T2DM). Recent clinical evidence suggests that retinal microvascular changes observed in diabetic patients reflect systemic vascular health, providing a non-invasive window into cardiovascular risk. This project proposes a deep learning–based framework for predicting CVD risk specifically using retinal fundus images collected from T2DM patients. Retinal datasets from IDRiD, Messidor-2, and APTOS 2019 were unified and reformulated into a binary cardiovascular risk screening task (Low Risk vs. High Risk). We conducted a comprehensive performance evaluation using multiple advanced architectures, including EfficientNet variants, ConvNeXt, RegNet, ResNext-50 and Transformer-based models (Swin, CoAtNet). Our results demonstrate that these models can effectively capture retinal biomarkers, with top-performing architectures achieving an accuracy of 93% and an AUC-ROC of 0.98. To ensure clinical interpretability, Grad-CAM visualization was implemented to highlight the specific retinal regions driving CVD risk prediction. This approach highlights the potential of retinal imaging as a scalable, cost-effective tool for early cardiovascular risk assessment in diabetic populations, facilitating timely intervention. Furthermore, the predictive performance can be further enhanced by integrating additional clinical data, such as patient history, laboratory parameters, and demographic information. |
| Keywords: | Type 2 Diabetes Mellitus (T2DM), Cardio Vascular Disease (CVD), Retinal Fundus Imaging, Micro |
| Cite Article: | "OcuCardio:Retinal Based Cardiovascular Risk Prediction System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b498-b502, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604204.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604204
Registration ID:211660
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b498-b502 Country: Visakhapatnam, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604204 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604204 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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