Journal article
Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques
Computers in Biology and Medicine, Vol.63, pp.124-132
2015
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.
Details
- Title
- Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques
- Authors/Creators
- G. Wang (Author/Creator) - Hong Kong Polytechnic UniversityK-M Lam (Author/Creator) - Tseung Kwan O HospitalZ. Deng (Author/Creator) - Jiangnan UniversityK-S Choi (Author/Creator) - Hong Kong Polytechnic University
- Publication Details
- Computers in Biology and Medicine, Vol.63, pp.124-132
- Publisher
- Elsevier
- Identifiers
- 991005545034007891
- Copyright
- © 2015 Elsevier Ltd.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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Source: InCites
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- Collaboration types
- Domestic collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.233 Pelvic & Renal Disorders
- 1.233.690 Bladder Cancer
- Web Of Science research areas
- Biology
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Mathematical & Computational Biology
- ESI research areas
- Computer Science