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ORIGINAL RESEARCH ARTICLE

Comparative study of transformer-based models for cardiovascular disease risk stratification with tabular biomarker data

Yuvraj Sharma1,2 ,  Ekta Tiwari3 ,  Neha Gupta2 ,  Mustafa Al-Maini4 ,  Rajesh Singh5 ,  Narendra N. Khanna6 ,  Vijay Rathore7 ,  Vandana Kumari8 ,  John R. Laird9 ,  George Kitas10,11 ,  Andrew Nicolaides12 ,  Zoltan Ruzsa13 ,  Aditya Sharma14 ,  Amer M. Johri15 ,  Laura E. Mantella16 ,  Gavino Faa17,18 ,  Esma R. Isenovic19 ,  Luca Saba20 ,  Jasjit S. Suri1,21,22,23*
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1 Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, California, United States of America
2 Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
3 Department of Innovation, Global Biomedical Technologies, Inc., Roseville, California, United States of America
4 Department of Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Ontario, Canada
5 Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, Uttarakhand, India
6 Department of Cardiology, Apollo Hospitals, New Delhi, India
7 Department of Nephrology, Kaiser Permanente, Sacramento, California, United States of America
8 School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
9 Department of Cardiology, St. Helena Hospital, St. Helena, California, United States of America
10 Academic Affairs, Dudley Group National Health Service Foundation Trust, Dudley, West Midlands, United Kingdom
11 Arthritis Research United Kingdom Epidemiology Unit, Manchester University, Manchester, Greater Manchester, United Kingdom
12 Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia, Cyprus
13 Invasive Cardiology Division, University of Szeged, Szeged, Hungary
14 Division of Cardiovascular Medicine, University of Virginia, Charlottesville, Virginia, United States of America
15 Department of Medicine, Queen’s University, Kingston, Ontario, Canada
16 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
17 Department of Biology, College of Science and Technology, Temple University, Philadelphia, Pennsylvania, United States of America
18 Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Sardinia, Italy
19 Department of Radiobiology and Molecular Genetics, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
20 Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Sardinia, Italy
21 University Center for Research and Development, Chandigarh University, Mohali, Punjab, India
22 Department of Computer Science, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Nagpur, Maharashtra, India
23 Department of Electrical and Computer Engineering, Idaho State University, Pocatello, Idaho, United States of America
AIH, 025460100 https://doi.org/10.36922/AIH025460100
Received: 14 November 2025 | Revised: 19 December 2025 | Accepted: 8 January 2026 | Published online: 4 February 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Cardiovascular disease (CVD) remains one of the leading causes of global mortality and disability. Advances in computational modeling and artificial intelligence have enhanced CVD risk prediction by integrating multivariate clinical and biochemical features. Recent developments in deep learning, especially transformer-based models for tabular data, have demonstrated superior capabilities in capturing nonlinear and high-dimensional biomarker interactions. This study proposes a predictive framework that uses statistical and clinical biomarker data to assess CVD risk. Traditional machine learning models (logistic regression, Gaussian Naïve Bayes, linear discriminant analysis, AdaBoost, and XGBoost) were compared with deep learning models (gated recurrent unit [GRU] and long short-term memory [LSTM]) and transformer-based models—self-attention and intersample attention transformer (SAINT), feature tokenizer (FT), and tab transformer. Experiments were conducted to evaluate the impact of data augmentation, analyze learning behavior through loss and accuracy curves, and assess a fusion approach combining tab transformer with recurrent networks. Model performance was evaluated using accuracy and receiver operating characteristic analysis. Transformer-based models consistently outperformed conventional machine learning and deep learning methods. SAINT and FT achieved an area under the curve (AUC) of 0.8875 and 0.9489, respectively. The tab transformer demonstrated the highest performance with an AUC of 0.9728. The fusion of the tab transformer with GRU and LSTM further enhanced predictive precision, improving representation learning and generalization for CVD risk prediction. The proposed transformer-based framework offers a robust, scalable, and interpretable solution for accurate CVD risk assessment. Its superior predictive capability highlights the potential for integration into clinical decision-support systems for early diagnosis and patient management.

Graphical abstract
Keywords
Cardiovascular disease
Deep learning
Transformer
Tab transformer
Machine learning
Risk prediction
Biomedical data
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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