Estimation and optimisation of right-censored data in survival analysis by neural network

Mohammad Ali Azadeh, Abbas Keramati, Hazhir Tolouei, Reza Parvari and Shima Pashapour

International Journal of Business Information Systems, 2013, vol. 14, issue 3, 322-334

Abstract: The main topic in medical statistics is survival time. It is difficult to obtain complete data in studies of survival time because of several reasons. One aspect of such difficulty is related to death of all patients. A study is often completed before the death of all patients. Moreover, incomplete information is kept with respect to death of all patients. Usually, exponential and Weibull distribution are used to estimate actual time of the censored data. In general, censored data models describe situations where there are variables of interest that cannot always be observed directly and may deviate from certain values. This study presents an adaptive neural network (ANN) approach for improved estimation of right censored data. To show the superiority and advantages of the ANN approach, it has been compared with fuzzy mathematical programming and conventional approaches. It is shown that ANN provides better estimation for right censored data in comparison to fuzzy mathematical programming and exponential and Weibull distributions. This is the first study that utilises ANN for improved estimation of right censored data.

Keywords: censored data; right-censored data; adaptive neural networks; survival analysis; fuzzy regression; optimisation; medical statistics; survival time; data modelling. (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:14:y:2013:i:3:p:322-334

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