Using artificial neural network to predict mortality of radical cystectomy for bladder cancer

Abstract

Surgical removal of bladder, i.e. radical cystectomy, is a standard treatment option for muscle invasive bladder cancer. Unfortunately, the treatment is associated with significant morbidities and mortalities. Many studies have been conducted to predict the morbidities and mortalities of radical cystectomy based on statistical analysis. In this paper, an artificial neural network is employed to predict 5-year mortality of radical cystectomy. The clinico-pathological data from a urology unit of a district hospital in Hong Kong were used to train and test the model. The outcome of the surgery was computed by an artificial neural network based on the risk factors identified by a conventional statistical method. It was found that the best overall accuracy of the neural network model was 77.8% and the 5-year mortality predicted by the model was comparable to that achieved by conventional statistical methods. The results of this study reflect that artificial intelligence has great development potential in medicine.