Document Type

Article

Publication Date

2024

DOI

10.1016/j.sasc.2024.200129

Publication Title

Systems and Soft Computing

Volume

6

Pages

200129 (1-10)

Abstract

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation not only yields superior predictive accuracy but also bears noteworthy consequences for patient care and accurate cardiac diagnosis. Through the efficient combination of these approaches, our method provides a powerful response to the difficulties presented by complicated cardiac data. The potential to transform illness prediction and prevention techniques and ultimately improve patient outcomes is demonstrated by this integration's success.

Rights

© 2024 The Authors

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

Data Availability

Article states: "Data will be made available on request."

Original Publication Citation

Gayathri, R., Sangeetha, S. K. B., Mathivanan, S. K., Rajadurai, H., Malar MB, B. A., Mallik, S., & Qin, H. (2024). Enhancing heart disease prediction with reinforcement learning and data augmentation. Systems and Soft Computing, 6, 1-10, Article 200129. https://doi.org/10.1016/j.sasc.2024.200129

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