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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of 2014 International Conference on Smart Computing, SMARTCOMP 2014
Number of pages7
ISBN (Electronic)9781479957118
Publication statusPublished - 17 Feb 2014
Event2014 1st International Conference on Smart Computing, SMARTCOMP 2014 - Hong Kong, Hong Kong
Duration: 3 Nov 20145 Nov 2014


Conference2014 1st International Conference on Smart Computing, SMARTCOMP 2014
Country/TerritoryHong Kong
CityHong Kong


  • Artificial neural network
  • Bladder cancer
  • Health informatics
  • Outcome prediction
  • Radical cystectomy

ASJC Scopus subject areas

  • Computer Science(all)

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