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Information Journal Paper

Title

PREDICTION OF INCOMPLETE DATA USING ARTIFICIAL NEURAL NETWORK

Pages

  21-28

Abstract

 Introduction: Suitable definition and realization of functional form of variables is required for a multivariate data analysis where in the number of parameters must be finite. In the study with INCOMPLETE DATA such as survival studies, performing an analysis is constraint to some assumptions, which may be not fulfilling in practical situations. In these cases, ARTIFICIAL NEURAL NETWORK (ANN) is recommended which is flexible and distribution free.Aim: This study aim to make a comparison between the PREDICTIONs of ANN and WEIBULL REGRESSION via a simulation study and based on a real data set example.Material and Method: At first, three random variables was generated from binomial and standardized normal distributions for simulation study. In addition, Weibull survival times were generated based on dependence structure of parameters and independent variables.Afterward, the data was randomly divided into two parts: training and testing data sets.Finally, network performance was assessed by using of least square error of PREDICTION and Bayesian information criterion.Results: Concordance index of ANN and WEIBULL REGRESSION was calculated as 0.812 and 0.785, respectively.Conclusion: The accuracy of ANN PREDICTION was better than that of Weibull PREDICTION.

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    APA: Copy

    BIGLARIAN, A., HAJIZADEH, E., & KAZEMNEJAD, A.. (2011). PREDICTION OF INCOMPLETE DATA USING ARTIFICIAL NEURAL NETWORK. JOURNAL OF SCIENCES (ISLAMIC AZAD UNIVERSITY), 20(78/2 (MATHEMATICS ISSUE)), 21-28. SID. https://sid.ir/paper/70430/en

    Vancouver: Copy

    BIGLARIAN A., HAJIZADEH E., KAZEMNEJAD A.. PREDICTION OF INCOMPLETE DATA USING ARTIFICIAL NEURAL NETWORK. JOURNAL OF SCIENCES (ISLAMIC AZAD UNIVERSITY)[Internet]. 2011;20(78/2 (MATHEMATICS ISSUE)):21-28. Available from: https://sid.ir/paper/70430/en

    IEEE: Copy

    A. BIGLARIAN, E. HAJIZADEH, and A. KAZEMNEJAD, “PREDICTION OF INCOMPLETE DATA USING ARTIFICIAL NEURAL NETWORK,” JOURNAL OF SCIENCES (ISLAMIC AZAD UNIVERSITY), vol. 20, no. 78/2 (MATHEMATICS ISSUE), pp. 21–28, 2011, [Online]. Available: https://sid.ir/paper/70430/en

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