Browsing by Author "Milosavljević, M. (7006876926)"

Influence of factors which fluctuate in time is usually described in term of residuals present in general linear or nonlinear models. The aim of this paper has been to identify relationship between mortality rates and major social and economic factors and to compare three methods in detecting (hidden) relations between them. Data on frequencies for all causes of death, e.g. mortality rates have been collected for Central Serbia for 21 years, from 1973. until 1993. We have used two models based on the general linear model, variance-covariance regression and correlation and Box-Jenkins time series method and one nonlinear model based on neural networks. The relationship has been established, according to our results. Among presented methods we consider the analysis based on multilayer neural networks techniques has several advantages over classical approach. © The authors 1996.

Influence of factors which fluctuate in time is usually described in term of residuals present in general linear or nonlinear models. The aim of this paper has been to identify relationship between mortality rates and major social and economic factors and to compare three methods in detecting (hidden) relations between them. Data on frequencies for all causes of death, e.g. mortality rates have been collected for Central Serbia for 21 years, from 1973. until 1993. We have used two models based on the general linear model, variance-covariance regression and correlation and Box-Jenkins time series method and one nonlinear model based on neural networks. The relationship has been established, according to our results. Among presented methods we consider the analysis based on multilayer neural networks techniques has several advantages over classical approach. © The authors 1996.

With the proliferation of potential prognostic factors for lung cancer, it is becoming increasingly more difficult to integrate the information provided by these factors into a single accurate prediction of clinical outcome. Here we reviewed five classification methods for their capabilities in classification of 200 patients with lung cancer into distinct prognostic groups using survival outcome as a criteria. The source of patient data for this study is a Lung Tumour Registry from Institute for Lung Diseases, University Clinical Hospital, Belgrade. Almost all developed classification algorithms determined prognostic groups according to biochemical tumour markers LDH and alkaline phosphatase, producing most significant split, instead of commonly used staging variables. The choice of which approach to use for a given classification problem depends not only on statistical properties of method, but also on medical considerations, such as whether more differential findings are given greater weight and the applicability of a classification rule. © 1997, The authors.

With the proliferation of potential prognostic factors for lung cancer, it is becoming increasingly more difficult to integrate the information provided by these factors into a single accurate prediction of clinical outcome. Here we reviewed five classification methods for their capabilities in classification of 200 patients with lung cancer into distinct prognostic groups using survival outcome as a criteria. The source of patient data for this study is a Lung Tumour Registry from Institute for Lung Diseases, University Clinical Hospital, Belgrade. Almost all developed classification algorithms determined prognostic groups according to biochemical tumour markers LDH and alkaline phosphatase, producing most significant split, instead of commonly used staging variables. The choice of which approach to use for a given classification problem depends not only on statistical properties of method, but also on medical considerations, such as whether more differential findings are given greater weight and the applicability of a classification rule. © 1997, The authors.