Machine Learning for Cardiovascular Disease Prediction and Diagnosis: A Systematic Review

DOI

10.2991/978-94-6463-704-5_10How to use a DOI?

Keywords

Machine learning; cardiovascular disease; prediction; diagnosis; healthcare AI

Abstract

The systematic ML application focus in this research aims to compare supervised and unsupervised Ml techniques. Using supervised ML methods deep learning, ensemble models and even traditional statistical approaches like logisitic regression have been incorporated. Key datasets chosen, along with the methods of feature selection and model evaluation procedures, including their interpretability are of utmost importance. How Ml models ameliorate risk stratification, automate ECG analasys, and enable personalized recommendations of treatment is also examined. To incorporate ethical and societal concerns pertaining to the reliance on AI technology, details regardong data heterogeneity and model cross-validation on ndiverse population are justified. The study’s intention was to pinpoint the focus areas, shortcomings, and findings of the most recent studies concentrating on AI and CVD diagnosis n ML. With the intention of outlining the scope of designs potentially beneficial for improving the accessibility and efficiency of healthcare systems, those that are reliant on AI have been identified. The integration of AI with CRVD diagnostics requires employing artificial Intelligence technologies where the reliability and dependence of the results on rigorous ML policies should be a focus.

Copyright

© 2025 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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TY  - CONF
AU  - Suman Kumar Swarnkar
AU  - Tien Anh Tran
PY  - 2025
DA  - 2025/04/30
TI  - Machine Learning for Cardiovascular Disease Prediction and Diagnosis: A Systematic Review
BT  - Proceedings of the International Conference on Smart Health and Intelligent Technologies (ICSHit-2024)
PB  - Atlantis Press
SP  - 106
EP  - 122
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-704-5_10
DO  - 10.2991/978-94-6463-704-5_10
ID  - Swarnkar2025
ER  -