Can AI Predict Heart Attack Risk?

Author: Rasit Dinc

Introduction

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, making the early and accurate prediction of events like heart attacks a critical focus of preventive medicine. Traditional risk assessment models, while valuable, have limitations in their predictive accuracy. However, the advent of artificial intelligence (AI) and machine learning (ML) is opening new frontiers in cardiovascular risk assessment, offering the potential for more personalized and precise predictions. This article explores the current landscape of AI in predicting heart attack risk, examining the methodologies, recent advancements, and future implications for healthcare professionals.

The Role of AI in Cardiovascular Risk Prediction

Artificial intelligence, particularly machine learning and deep learning algorithms, can analyze vast and complex datasets to identify patterns and predictors that may be missed by conventional statistical methods. In the context of heart attack risk, AI models can integrate a wide array of data points, including clinical information, demographic data, laboratory results, and imaging data, to generate a more holistic and individualized risk profile.

A 2024 study published in Electronics demonstrated the potential of AI to predict heart attacks up to ten years in advance [1]. The researchers developed and tested an AI-based tool that combined and compared multiple AI methods to determine a personalized heart attack probability. Their findings indicated that models based on logistic regression were the most accurate, and that predictions could be made using a reduced set of parameters, including heart rate, age, Body Mass Index (BMI), and cholesterol levels. This highlights the potential for developing preventive strategies based on modifiable risk factors, monitored by AI-powered systems.

Advancements in AI-Powered Diagnostics

Recent advancements have focused on leveraging readily available diagnostic tools, such as the electrocardiogram (ECG), to screen for heart attack risk. Researchers at Yale School of Medicine developed an AI tool that can identify individuals at high risk of developing heart failure by analyzing ECG images [2]. This innovative approach uses a widely available and non-invasive test to provide early risk stratification, which could significantly reduce hospitalizations and premature deaths. The model was validated across diverse populations in the United States, the United Kingdom, and Brazil, demonstrating its potential for large-scale, equitable implementation.

Furthermore, the application of automated machine learning (AutoML) is democratizing the development of sophisticated predictive models. A 2025 study in Scientific Reports showcased how AutoML can enhance cardiovascular risk assessment by processing large datasets and creating tailored models without the need for extensive data science expertise [3]. The study identified key determinants of CVDs, such as age, lipoprotein(a), troponin T, BMI, and cholesterol, with a high degree of accuracy. This research underscores the power of AutoML to build robust predictive models for cardiovascular mortality and adapt them to different clinical settings.

Challenges and Future Directions

Despite the promising results, the integration of AI into clinical practice is not without its challenges. These include the need for large, high-quality datasets for model training and validation, the “black box” nature of some deep learning models, and the importance of ensuring data privacy and security. Moreover, the ethical implications of AI-driven predictions, including the potential for bias and the impact on patient anxiety, must be carefully considered.

Looking ahead, the future of AI in cardiology is bright. The continued development of explainable AI (XAI) will help to demystify the decision-making process of these complex algorithms, fostering greater trust and acceptance among clinicians. The integration of AI with wearable technology and other real-time data sources will enable continuous risk monitoring and personalized interventions. As these technologies mature, they will undoubtedly become an indispensable tool for healthcare professionals in the fight against cardiovascular disease.

Conclusion

The evidence strongly suggests that AI can indeed predict heart attack risk with increasing accuracy and sophistication. By leveraging the power of machine learning and deep learning, AI-powered tools can analyze complex data to provide early and personalized risk assessments. While challenges remain, the ongoing advancements in this field hold the promise of a new era in preventive cardiology, where data-driven insights empower clinicians to intervene earlier and more effectively, ultimately saving lives.

References

[1] Rojek, I., Kotlarz, P., Kozielski, M., & Jagodziński, M. (2024). Development of AI-Based Prediction of Heart Attack Risk as an Element of Preventive Medicine. Electronics, 13(2), 272. https://doi.org/10.3390/electronics13020272

[2] Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Asselbergs, F. W., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., Krumholz, H. M., Oikonomou, E. K., & Khera, R. (2025). Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. European Heart Journal. https://doi.org/10.1093/eurheartj/ehae914

[3] Bibi, I., Schaffert, D., Blanke, P., Illian, L., Lenzing, F., Martin, N., Leipe, J., März, W., Stach, K., & Olsavszky, V. (2025). Cardiovascular risk assessment enhanced by automated machine learning in a multi-phase study. Scientific Reports, 15(1), 36474. https://doi.org/10.1038/s41598-025-24189-z