Hierarchical and Contrastive Learning for Multilateral Personalized Cardiovascular Detection Using Retinal and Cardiovascular Biomarkers

  • Authors

    • Haritha Tummala Research Scholar, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India
    • Dr.M. Kavitha Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India
    https://doi.org/10.14419/ppqz8427

    Received date: June 22, 2025

    Accepted date: August 25, 2025

    Published date: September 16, 2025

  • Cardiovascular Detection; Heart Attacks; Hierarchical; Retinal

  • Abstract

    Cardiovascular diseases, such as heart attacks, kill millions of people each year. This is a major health problem around the world. Current ‎tools like the Framingham Risk Score fail in many cases. These tools do not work well for all types of people. This happens because individual differences are not considered. This paper presents a new model. It uses deep learning to predict heart attacks. The model uses data ‎from two sources: the eye and the heart. It combines features from retinal images and cardiovascular signals. Retinal images show blood ‎vessels in the eye. These vessels give clues about the body's microvascular health. Heart signals, including heart rate variability, reflect the ‎condition of the heart. The model uses several advanced parts. First, a graph transformer looks at blood vessel shapes in the retina. Then, ‎another transformer processes the heart signals over time. These features are fused using a special fusion technique. It finds the most useful ‎combined features from both sources. Then, a genetic algorithm selects the best features. It keeps the features that are most useful and easiest to ‎understand. Finally, a contrastive classifier sorts patients into risk groups. The model achieves very high accuracy. The model gives results ‎that doctors understand. It uses SHAP and Grad-CAM to show which features matter most. This helps doctors trust the model. The system ‎is easy to use in real clinics. It does not need blood tests or invasive tools. Just eye images and heart signals are needed. This model is powerful, fast, and easy to use. It helps doctors find heart risks early. It works well for many different people. It is a good tool for preventing ‎heart attacks. The approach suggests it has the potential to transform heart screening practices‎.

  • References

    1. J. Mackay and G. A. Mensah, The atlas of heart disease and stroke. World Health ‎Organization, 2004.‎
    2. ‎S. Sheridan, M. Pignone, and C. Mulrow, “Framingham-based tools to calculate the global risk ‎of coronary heart disease: a systematic review of tools for clinicians,” Journal of general ‎internal medicine, vol. 18, no. 12, pp. 1039–1052, 2003.‎https://doi.org/10.1111/j.1525-1497.2003.30107.x.
    3. Khan, Muhammad Shahzeb, Izza Shahid, Ahmed Bennis, Amina Rakisheva, Marco Metra, ‎and Javed Butler. "Global epidemiology of heart fail-ure." Nature Reviews Cardiology 21, no. ‎‎10, 717-734, 2024.‎https://doi.org/10.1038/s41569-024-01046-6.
    4. ‎A. Pramod, H. S. Naicker, and A. K. Tyagi, “Machine learning and deep learning: Open issues ‎and future research directions for the next 10 years,” Computational analysis and deep ‎learning for medical care: Principles, methods, and applications, pp. 463–490, 2021.‎https://doi.org/10.1002/9781119785750.ch18.
    5. ‎J. A. Damen, L. Hooft, E. Schuit, T. P. Debray, G. S. Collins, I. Tzoulaki, C. M. Lassale, G. C. ‎Siontis, V. Chiocchia, C. Roberts, et al., “Prediction models for cardiovascular disease risk in ‎the general population: systematic review,” bmj, vol. 353, 2016.‎https://doi.org/10.1136/bmj.i2416.
    6. ‎G. Ernst, “Hidden signals—the history and methods of heart rate variability,” Frontiers in ‎public health, vol. 5, p. 265, 2017.‎https://doi.org/10.3389/fpubh.2017.00265.
    7. ‎D.-Y. Yu, P. Yu, C. Balaratnasingam, S. Cringle, E.-N. Su, A. M´endezVilas, J. D´ıaz, et al., ‎‎“Microscopic structure of the retina and vasculature in the human eye,” Microscopy: Science, ‎Technology, Applications and Education, vol. 867, p. 875, 2010.‎
    8. ‎Chen, Xiaojuan, Luyu Niu, and Song Guo. "Efficient retinal artery/vein classification with ‎dense color-invariant feature learning." Neural Compu-ting and Applications 37, no. 3, 1255-‎‎1270, 2025.‎https://doi.org/10.1007/s00521-024-10696-z.
    9. ‎A. Avolio, “Arterial stiffness,” Pulse, vol. 1, no. 1, pp. 14–28, 2013.‎https://doi.org/10.1159/000348620.
    10. ‎P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” ‎IEEE transactions on medical imaging, vol. 35, no. 11, pp. 2369–2380, 2016.‎https://doi.org/10.1109/TMI.2016.2546227.
    11. W. C. Lang and K. Forinash, “Time-frequency analysis with the continuous wavelet ‎transform,” American journal of physics, vol. 66, no. 9, pp. 794–797, 1998.‎https://doi.org/10.1119/1.18959.
    12. ‎F. Liu and G. Zeng, “Study of genetic algorithm with reinforcement learning to solve the tsp,” ‎Expert Systems with Applications, vol. 36, no. 3, pp. 6995–7001, 2009.‎https://doi.org/10.1016/j.eswa.2008.08.026.
    13. Q. Zhang, D. Zhou, and X. Zeng, “Heartid: A multiresolution convolutional neural network for ‎ecg-based biometric human identification in smart health applications,” Ieee Access, vol. 5, ‎pp. 11805–11816, 2017.‎https://doi.org/10.1109/ACCESS.2017.2707460.
    14. ‎V. C.-R. Hsieh, T.-N. Wu, S.-H. Liu, and S.-H. Shieh, “Referral-free health care and delay in ‎diagnosis for lung cancer patients,” Japanese Journal of Clinical Oncology, vol. 42, no. 10, pp. ‎‎934–939, 2012.‎https://doi.org/10.1093/jjco/hys113.
    15. M. Chew et al.,” AI for Retinal Vessel Analysis,” JAMA Cardiology, 2020.‎
    16. C. Danielescu, M. G. Dabija, A. H. Nedelcu, V. V. Lupu, A. Lupu, I. Ioniuc, G.-E. Gˆılc˘a-‎Blanariu, V.-C. Donica, M.-L. Anton, and O. Musat, “Automated retinal vessel analysis based ‎on fundus photographs as a predictor for non-ophthalmic diseases—evolution and ‎perspectives,” Journal of Personalized Medicine, vol. 14, no. 1, p. 45, 2023.‎https://doi.org/10.3390/jpm14010045.
    17. Z. Girach, A. Sarian, C. Maldonado-Garc´ıa, N. Ravikumar, P. I. Sergouniotis, P. M. Rothwell, ‎A. F. Frangi, and T. H. Julian, “Retinal imaging for the assessment of stroke risk: a systematic ‎review,” Journal of Neurology, vol. 271, no. 5, pp. 2285–2297, 2024.‎https://doi.org/10.1007/s00415-023-12171-6.
    18. ‎K. Muthukumar, D. Nandi, P. Ranjan, K. Ramachandran, S. Pj, A. Ghosh, A. M, A. ‎Radhakrishnan, V. Dhandapani, and R. Janardhanan, “Inte-grating electrocardiogram and ‎fundus images for early detection of cardiovascular diseases,” Scientific Reports, vol. 15, no. ‎‎1, p. 4390, 2025.‎https://doi.org/10.1038/s41598-025-87634-z.
    19. E. Y. Chew, S. A. Burns, A. G. Abraham, M. F. Bakhoum, J. A. Beckman, T. Y. Chui, R. P. ‎Finger, A. F. Frangi, R. F. Gottesman, M. B. Grant et al., “Standardization and clinical ‎applications of retinal imaging biomarkers for cardiovascular disease: a roadmap from an ‎nhlbi workshop,” Nature Reviews Cardiology, vol. 22, no. 1, pp. 47–63, 2025.‎https://doi.org/10.1038/s41569-024-01060-8.
    20. A. Muhammad, R. N. Maharani, and A. Oddang, “The relationship between blood sugar and ‎severity of diabetic retinopathy in type 2 diabetes mellitus patients,” JurnalEduHealth, vol. ‎‎15, no. 03, pp. 195–200, 2024.‎https://doi.org/10.55047/tires.v2i1.1136.
    21. I. Rojek, P. Kotlarz, M. Kozielski, M. Jagodzi´nski, and Z. Kr´olikowski, “Development of ai-‎based prediction of heart attack risk as an element of preventive medicine,” Electronics, vol. ‎‎13, no. 2, p. 272, 2024.‎https://doi.org/10.3390/electronics13020272.
    22. I. J. Selvam, M. Madhavan, and S. K. Kumarasamy, “Detection and classification of ‎electrocardiography using hybrid deep learning models,” Hel-lenic Journal of Cardiology, vol. ‎‎81, pp. 75–84, 2025.‎https://doi.org/10.1016/j.hjc.2024.08.011.
    23. J.-H. Han, “Artificial intelligence in eye disease: recent developments, applications, and ‎surveys,” p. 1927, 2022.‎https://doi.org/10.3390/diagnostics12081927.
    24. K. M. Anderson and D. M. Anderson, “Lncrnas at the heart of development and disease,” ‎Mammalian Genome, vol. 33, no. 2, pp. 354–365, 2022.‎https://doi.org/10.1007/s00335-021-09937-6.
    25. J. de Bont, S. Jaganathan, M. Dahlquist, ˚A. Persson, M. Stafoggia, and P. Ljungman, ‎‎“Ambient air pollution and cardiovascular diseases: An um-brella review of systematic reviews ‎and meta-analyses,” Journal of internal medicine, vol. 291, no. 6, pp. 779–800, 2022.‎https://doi.org/10.1111/joim.13467.
    26. L. Zhou, S. Tang, F. Li, Y. Wu, S. Li, L. Cui, J. Luo, L. Yang, Z. Ren, J. Zhang et al., “Ceria ‎nanoparticles prophylactic used for renal ischemia-reperfusion injury treatment by attenuating ‎oxidative stress and inflammatory response,” Biomaterials, vol. 287, p. 121686, 2022.‎https://doi.org/10.1016/j.biomaterials.2022.121686.
    27. ‎Ummiti Sreenivasulu, Shaik Fairooz, R. Anil Kumar, Sarala Patchala, R. Prakash ‎Kumar, Adireddy‎Rmaesh, “Joint beamforming with RIS assisted MU-MISO ‎systems using HR-mobilenet and ASO ‎algorithm, Digital Signal Processing, Volume ‎‎159, 2025, 104955,ISSN 1051-2004, ‎https://doi.org/10.1016/j.dsp.2024.104955.
    28. Satyam, A. ., Kumar, R. A. ., Patchala , S. ., Pachala, S. ., Geeta Bhimrao Atkar, &Mahalaxm, U. ‎S. ‎B. K. . (2025). Multi-agent learning for UAV networks: a unified approach ‎to trajectory control, ‎‎frequency allocation and routing. International Journal of Basic and Applied Sciences, 14(2), 189-‎‎‎201. https://doi.org/10.14419/474dfq89.
    29. Kumar B. P. Mahalaxmi،‎ U. S. B. K., Nagagopiraju, V., Manda, A. K., Chandana, K., Betam, D. ‎‎‎Suresh., Gangadhar, A., &Patchala, D. S. (2025). Deep Reinforcement Learning for Joint UAV ‎‎Trajecto‎ry and Communication Design in Cache-Enabled Cellular Networks. International Journal ‎of ‎Basic and Applied Sciences, 14(3), 418-430. https://doi.org/10.14419/djn77m90.

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  • How to Cite

    Tummala , H. ., & Kavitha , D. . (2025). Hierarchical and Contrastive Learning for Multilateral Personalized Cardiovascular Detection Using Retinal and Cardiovascular Biomarkers. International Journal of Basic and Applied Sciences, 14(5), 567-577. https://doi.org/10.14419/ppqz8427