Enhancing Explainability in Cardiac Arrest Systems – A Novel Ensemble Approach using AI

DOI

10.2991/978-94-6463-738-0_29How to use a DOI?

Keywords

Machine Learning Algorithms; Random Forest; Decision Tree; Neural Networks; Support Vector Machine; Accuracy

Abstract

Cardiovascular diseases are a major burden to health systems in the world and are the leading cause of death and morbidity. Disease diagnosis is a critical component in disease management and prevention, if is done correctly within the shortest time. However, routine medical practices from time-to-time experience challenges such as wrong diagnoses, and delayed treatment, which in turn costs the patients much money and in the process their health will have deteriorated. The conventional methods are recommended to be addressed with the help of Machine learning (ML) algorithms which can provide more precise diagnostic information and lessen mistakes. Recognized to overcome the hurdles in terms of accurate identification, there is a rising concern in applying medicine based on ML methods for interpretation of healthcare data and helping healthcare decisions. This paper employs artificial intelligence via a machine learning based model computations on large patient records dataset to recommend an approach to estimating the probability of heart disease for heart patient. Random forests, neural networks and support vector machines particularly are evaluated and optimized to their accuracy of pattern detection and prediction. Accuracy and reliability of the developed model are measured by assessment parameters including specificity and sensitivity indices coupled with the area under the curve of the receiver operating characteristic chart. Model is established to have a superior performance than the traditional risk assessment techniques and provides a precise and personalized risk prognosis of heart disease.

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  - Yuka Chauhan
AU  - Navnish Goel
AU  - Aditya Agarwal
AU  - Abhishek Rai
PY  - 2025
DA  - 2025/06/22
TI  - Enhancing Explainability in Cardiac Arrest Systems – A Novel Ensemble Approach using AI
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 345
EP  - 359
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_29
DO  - 10.2991/978-94-6463-738-0_29
ID  - Chauhan2025
ER  -