UNASSIGNED: @entity150 is a common @entity5 in @entity5 . For muscle invasive @entity150 , surgical @entity1494 , 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. @entity5 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 @entity1 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.

Prediction of mortality after radical cystectomy for XXXX by machine learning techniques.

@entity5 :: ('MESH:D009369', 'Disease') :: ['cancer', 'genitourinary malignancy'] @entity1 :: ('9606', 'Species') :: ['patients'] @entity150 :: ('MESH:D001749', 'Disease') :: ['Bladder cancer', 'bladder cancer'] @entity1494 :: ('MESH:D001745', 'Disease') :: ['removal of the bladder']
@entity150 :: (MESH:D001749,Disease) :: ['bladder cancer']
