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Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques

Wang, G.ORCID: 0000-0002-5258-0532, Lam, K-M, Deng, Z. and Choi, K-S (2015) Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques. Computers in Biology and Medicine, 63 . pp. 124-132.

Link to Published Version: https://doi.org/10.1016/j.compbiomed.2015.05.015
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Abstract

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making.

Item Type: Journal Article
Publisher: Elsevier
Copyright: © 2015 Elsevier Ltd.
URI: http://researchrepository.murdoch.edu.au/id/eprint/52385
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