Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)

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Cardiovascular Risk Prediction in Indian Youth Using Hybrid CNN–LSTM Approach with Feature Augmentation

Authors
Shital Solanki1, *, Dipesh Makwana2
1Department of Information Technology, L.D. College of Engineering, Ahmedabad, Gujarat, India
2Department of Instrumentation and Control, L.D. College of Engineering, Ahmedabad, Gujarat, India
*Corresponding author. Email: shital@ldce.ac.in
Corresponding Author
Shital Solanki
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-628-9_4How to use a DOI?
Keywords
Cardiovascular disease; NFHS-5; Accuracy; CNN–LSTM; feature augmentation
Abstract

Cardiovascular disease is no longer confined to older populations is increasingly observed among young adults due to changing dietary habits, reduced physical activity, and persistent psychosocial stress. Conventional statistical and machine learning models often underperform in capturing nonlinear and sequential dependencies inherent in health and lifestyle data. In this study, National Family Health Survey–5 (NFHS-5) dataset used to build hybrid deep learning models that combine convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). In addition to standard features such as BMI, blood pressure, and anemia status, Feature augmentation techniques were applied to generate composite nutritional and lifestyle features. Comparative experiments demonstrated that the CNN–LSTM hybrid approach achieved superior performance with accuracy 92.1%, AUROC 0.91 compared to Logistic Regression, Random Forest, XGBoost, and standalone CNN or LSTM models. These results highlight the potential of hybrid deep learning for youth-focused preventive health in India.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
Series
Advances in Engineering Research
Publication Date
31 March 2026
ISBN
978-94-6239-628-9
ISSN
2352-5401
DOI
10.2991/978-94-6239-628-9_4How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shital Solanki
AU  - Dipesh Makwana
PY  - 2026
DA  - 2026/03/31
TI  - Cardiovascular Risk Prediction in Indian Youth Using Hybrid CNN–LSTM Approach with Feature Augmentation
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 28
EP  - 38
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_4
DO  - 10.2991/978-94-6239-628-9_4
ID  - Solanki2026
ER  -