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Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

Salah Alzghoul1,*, Othman Smadi1, Ali Al Bataineh2, Mamon Hatmal3, Ahmad Alamm4

1 Biomedical Engineering Department, The Hashemite University, Zarqa, Jordan
2 Department of Electrical and Computer Engineering, Norwich University, Northfield, Vermont, 05663, USA
3 Department of Biochemistry and Molecular Biology, The Hashemite University, Zarqa, Jordan
4 National Centre for Big Data Science and Artificial Intelligence, Amman, Jordan

* Corresponding Author: Salah Alzghoul. Email: email

Intelligent Automation & Soft Computing 2023, 37(1), 455-470. https://doi.org/10.32604/iasc.2023.038338

Abstract

Currently, the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis (AS) is made by healthcare professionals based on the patient’s clinical biometric records. A delay in surgical aortic valve replacement (SAVR) can potentially affect patients’ quality of life. By using ML algorithms, this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery. This study represents a novel approach that has the potential to improve decision-making and, ultimately, improve patient outcomes. We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or were indicated for SAVR. We divide the data into two groups: those who died within the first year after SAVR and those who survived for more than one year or were still alive at the last follow-up. We then use six different ML algorithms, Support Vector Machine (SVM), Classification and Regression Tree (C and R tree), Generalized Linear (GL), Chi-Square Automatic Interaction Detector (CHAID), Artificial Neural Network (ANN), and Linear Regression (LR), to generate predictions for the best timing for SAVR. The results showed that the SVM algorithm is the best model for predicting the optimal timing for SAVR and for predicting the post-surgery survival period. By optimizing the timing of SAVR surgery using the SVM algorithm, we observed a significant improvement in the survival period after SAVR. Our study demonstrates that ML algorithms generate reliable models for predicting the optimal timing of SAVR in asymptomatic patients with moderate-to-severe AS.

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APA Style
Alzghoul, S., Smadi, O., Bataineh, A.A., Hatmal, M., Alamm, A. (2023). Machine learning prediction models of optimal time for aortic valve replacement in asymptomatic patients. Intelligent Automation & Soft Computing, 37(1), 455-470. https://doi.org/10.32604/iasc.2023.038338
Vancouver Style
Alzghoul S, Smadi O, Bataineh AA, Hatmal M, Alamm A. Machine learning prediction models of optimal time for aortic valve replacement in asymptomatic patients. Intell Automat Soft Comput . 2023;37(1):455-470 https://doi.org/10.32604/iasc.2023.038338
IEEE Style
S. Alzghoul, O. Smadi, A.A. Bataineh, M. Hatmal, and A. Alamm "Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients," Intell. Automat. Soft Comput. , vol. 37, no. 1, pp. 455-470. 2023. https://doi.org/10.32604/iasc.2023.038338



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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