title: Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.
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.
authors.affiliation: School of Nursing, Hong Kong Polytechnic University, Hong Kong, China; Centre for Smart Health, School of Nursing, Hong Kong Polytechnic University, Hong Kong, China. Electronic address: guanjin.br.wang@connect.polyu.hk.
authors.forename: Guanjin
authors.initials: G
authors.lastname: Wang
authors.affiliation: Department of Surgery, Tseung Kwan O Hospital, Hong Kong, China.
authors.forename: Kin-Man
authors.initials: KM
authors.lastname: Lam
authors.affiliation: School of Digital Media, Jiangnan University, Wuxi, Jiangsu, China.
authors.forename: Zhaohong
authors.initials: Z
authors.lastname: Deng
authors.affiliation: School of Nursing, Hong Kong Polytechnic University, Hong Kong, China; Centre for Smart Health, School of Nursing, Hong Kong Polytechnic University, Hong Kong, China; Interdisciplinary Division of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong, China.
authors.forename: Kup-Sze
authors.initials: KS
authors.lastname: Choi
mesh_terms: D000368:Aged; D015653:Cystectomy; D016208:Databases, Factual; D018572:Disease-Free Survival; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011237:Predictive Value of Tests; D060388:Support Vector Machine; D015996:Survival Rate; D001749:Urinary Bladder Neoplasms / Q000401:mortality* / Q000601:surgery*
keywords: Bladder cancer; Machine learning; Mortality; Prediction; Prognosis; Radical cystectomy