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Garima Singh,
Anurag Singh,
- M. Tech Scholar, Department of Computer Science and Engineering, KIPM College of Engineering & Technology, GIDA, Gorakhpur, Uttar Pradesh, India
- Assistant professor, Department of Computer Science and Engineering, KIPM College of Engineering & Technology, GIDA, Gorakhpur, Uttar Pradesh, India
Abstract
Cardiovascular diseases (CVDs) are the prevalent morbid and fatal conditions worldwide. Repetition is key to stopping these diseases before they start and before they appear. With the advancement of data analytics and AI, machine learning (ML) has emerged as a powerful tool for predicting CVD risk and delivering personalized healthcare solutions. This review comprehensively summarizes the latest trends in the use of machine learning techniques to predict cardiovascular disease risk. This paper evaluates various ML algorithms, including supervised learning, unsupervised learning, ensemble, and deep learning algorithms, regarding their capability to handle clinical and non-clinical data. Another critical consideration the article underlines is the appropriateness of employing feature selection, model evaluation metrics, and interpretability to ensure reliable, clinically applicable predictive models. The paper also discusses problems with imbalanced data, privacy issues, and how these models can be incorporated into a clinical setting. The review discusses the future direction of this field in terms of broader impact, including pressure from ‘big’ data sets and model generalisation, and cross-disciplinary exchanges between a physician and a computer scientist, so that ML-based cardiovascular risk 2 prediction systems can be successfully deployed. In this review, we summarise the potential of machine learning for automating CVD prevention, which should lead to earlier detection , individualised treatments, and improved healthcare outcomes.
Keywords: cardiovascular diseases (CVDs), Machine learning (ML), Data analytics
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| Volume | 04 | |
| 02 | ||
| Received | 18/07/2026 | |
| Accepted | 10/10/2026 | |
| Published | 20/10/2026 | |
| Publication Time | 94 Days |