Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Health and Social Sciences | en_US |
| dc.creator | Lam, Kin Man | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/9221 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Using artificial neural network to predict the mortality of radical cystectomy for bladder cancer | en_US |
| dcterms.abstract | Bladder cancer is a common cancer in Hong Kong. Definitive treatment of muscle invasive bladder cancer is surgical removal of bladder, i.e. radical cystectomy. This surgery carries significant morbidities and mortalities. Many researches tried to look for factors to predict the morbidities and mortalities of radical cystectomy. Traditional research identified the factors by statistical method. The present research project employed artificial neural network to predict the 5-year mortality of radical cystectomy. The clinico-pathological data from a urology unit of a district hospital in Hong Kong was collected. Risk factors were identified by conventional statistical method. Outcome of surgery was computed by artificial neural network by using the risk factors identified in statistics. The best overall accuracy of the neural network was 77.8%. The 5-year mortality predicted by neural network was comparable to conventional statistical model. The result of this study reflects the potential development of artificial intelligence in health science. | en_US |
| dcterms.extent | vii, 55 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2014 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.LCSH | Bladder -- Cancer | en_US |
| dcterms.LCSH | Bladder -- Cancer -- Treatment | en_US |
| dcterms.LCSH | Cancer -- Mortality | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022020058003411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.39 MB | Adobe PDF | View/Open |
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