Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection
Omneya Attallah, Alan Karthikesalingam, Peter J.E. Holt, Matthew M. Thompson, Rob Sayers, Matthew J. Bown, Eddie C. Choke, Xianghong Ma*
Mechanical Engineering & Design
Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
Copyright © 2017 by Institution of Mechanical Engineers
Cox's proportional hazard model
endovascular aortic repair
hybrid feature selection
Multiple classifier system
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Repair Engineering & Materials Science
Feature extraction Engineering & Materials Science
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Logistics Engineering & Materials Science
Support vector machines Engineering & Materials Science
Attallah, O., Karthikesalingam, A., Holt, P. J. E., Thompson, M. M., Sayers, R., Bown, M. J., Choke, E. C., & Ma, X. (2017). Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 231(11), 1048-1063. https://doi.org/10.1177/0954411917731592
Attallah, Omneya ; Karthikesalingam, Alan ; Holt, Peter J.E. ; Thompson, Matthew M. ; Sayers, Rob ; Bown, Matthew J. ; Choke, Eddie C. ; Ma, Xianghong. / Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection. In: Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 2017 ; Vol. 231, No. 11. pp. 1048-1063.
@article{9de2cfd624dc466487c26f294cc8be7c,
title = "Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection",
abstract = "Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.",
keywords = "censoring, Cox's proportional hazard model, endovascular aortic repair, hybrid feature selection, Multiple classifier system, survival analysis",
author = "Omneya Attallah and Alan Karthikesalingam and Holt, {Peter J.E.} and Thompson, {Matthew M.} and Rob Sayers and Bown, {Matthew J.} and Choke, {Eddie C.} and Xianghong Ma",
note = "Copyright {\textcopyright} 2017 by Institution of Mechanical Engineers",
journal = "Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine",
publisher = "SAGE",
Attallah, O, Karthikesalingam, A, Holt, PJE, Thompson, MM, Sayers, R, Bown, MJ, Choke, EC & Ma, X 2017, 'Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection', Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 231, no. 11, pp. 1048-1063. https://doi.org/10.1177/0954411917731592
Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection. / Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J.E.; Thompson, Matthew M.; Sayers, Rob; Bown, Matthew J.; Choke, Eddie C.; Ma, Xianghong.
In: Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, Vol. 231, No. 11, 01.11.2017, p. 1048-1063.
T1 - Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection
AU - Attallah, Omneya
AU - Karthikesalingam, Alan
AU - Holt, Peter J.E.
AU - Thompson, Matthew M.
AU - Sayers, Rob
AU - Bown, Matthew J.
AU - Choke, Eddie C.
AU - Ma, Xianghong
N1 - Copyright © 2017 by Institution of Mechanical Engineers
N2 - Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.
AB - Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.
KW - censoring
KW - Cox's proportional hazard model
KW - endovascular aortic repair
KW - hybrid feature selection
KW - Multiple classifier system
KW - survival analysis
JO - Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
JF - Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
Attallah O, Karthikesalingam A, Holt PJE, Thompson MM, Sayers R, Bown MJ et al. Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 2017 Nov 1;231(11):1048-1063. https://doi.org/10.1177/0954411917731592