A Secure Remote Health Monitoring for Heart Disease Prediction Using Machine Learning and Deep Learning Techniques in Explainable Artificial Intelligence Framework-Reference-Cited by-同舟云学术

A Secure Remote Health Monitoring for Heart Disease Prediction Using Machine Learning and Deep Learning Techniques in Explainable Artificial Intelligence Framework

Published:2023-11-15 Issue: Volume: Page:
ISSN:
Container-title:The 10th International Electronic Conference on Sensors and Applications
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Author:

Patro Sibo Prasad¹,Padhy Neelamadhab¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, GIET University, Gunupur 765022, India

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/58/1/78/pdf

Reference10 articles.

1. Ordikhani, M., Saniee Abadeh, M., Prugger, C., Hassannejad, R., Mohammadifard, N., and Sarrafzadegan, N. (2022). An evolutionary machine learning algorithm for cardiovascular disease risk prediction. PLoS ONE, 17.

2. Guleria, P., Naga Srinivasu, P., Ahmed, S., Almusallam, N., and Alarfaj, F.K. (2022). XAI framework for cardiovascular disease prediction using classification techniques. Electronics, 11.

3. Ahsan, M. (2022). Heart Attack Prediction Using Machine Learning and XAI. [Ph.D. Thesis, Brac University].

4. Moreno-Sanchez, P.A. (2020, January 10–13). Development of an explainable prediction model of heart failure survival by using ensemble trees. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.

5. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond;Yang;Inf. Fusion,2022

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