Back to Search Start Over

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Authors :
Mehmet Akif Bülbül
Mehmet Fatih Işık
Source :
Biomimetics, Vol 9, Iss 5, p 304 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients’ clinical characteristics and findings to determine treatment plans, making accurate predictions essential for efficient resource utilization and optimal patient support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, and the Softmax Classifier was developed for predicting patient survival. The architecture was evaluated using the Haberman’s Survival dataset and the Echocardiogram dataset from UCI. The results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, and Gradient Bagging applied to the same datasets. The findings indicate that the proposed architecture outperforms other Machine Learning methods in predicting patient survival for both datasets and surpasses the results reported in the literature for the Haberman’s Survival dataset. In the light of the findings obtained, the models obtained with the proposed architecture can be used as a decision support system in determining patient care and applied methods.

Details

Language :
English
ISSN :
23137673
Volume :
9
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Biomimetics
Publication Type :
Academic Journal
Accession number :
edsdoj.0fe25594634843ae93b66135d3db7880
Document Type :
article
Full Text :
https://doi.org/10.3390/biomimetics9050304