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    Öğe
    Artificial neural networks
    (Ortadogu Ad Pres & Publ Co, 2007) Yazici, Ayse Canan; Oegues, Ersin; Ankarali, Seyit; Canan, Sinan; Ankarali, Handan; Akkus, Zeki
    Artificial neural networks (ANNs) are computer softwares that were developed by simulating the working mechanism of human brain to accomplish the basic functions of the brain. ANNs have capability to learn, remember and then generalize the data to produce new information, and to detect the relationships between variables. There are considerable relations between the statistical methods and the neural networks. In the present study, biological neural network and neurons of the human brain and the general structure of ANNs were introduced. Then ANNs' relations with the statistical methods were investigated. ANNs' advantages and disadvantages as statistical methods were discussed. Many neural networks methods are considered generalizations of some of the classical statistical techniques. Generally, in statistics ANNs are used as flexible, nonlinear regression and classification models. Many neural network architectures have close links with the nonparametric statistical methods. Results may be obtained by training the feed forward ANNs algorithms with the nonlinear models of many statistical techniques.
  • [ X ]
    Öğe
    Comparison of logistic regression model and classification tree: An application to postpartum depression data
    (Pergamon-Elsevier Science Ltd, 2007) Camdeviren, Handan Ankarali; Yazici, Ayse Canan; Akkus, Zeki; Bugdayci, Resul; Sungur, Mehmet Ali
    In this study, it is aimed that comparing logistic regression model with classification tree method in determining social-demographic risk factors which have effected depression status of 1447 women in separate postpartum periods. In determination of risk factors, data obtained from prevalence study of postpartum depression were used. Cut-off value of postpartum depression scores that calculated was taken as 13. Social and demographic risk factors were brought up by helping of the classification tree and logistic regression model. According to optimal classification tree total of six risk factors were determined, but in logistic regression model 3 of their effect, were found significantly. In addition, during the relations among risk factors in tree structure were being evaluated, in logistic regression model corrected main effects belong to risk factors were calculated. In spite of, classification success of maximal tree was found better than both optimal tree and logistic regression model, it is seen that using this tree structure in practice is very difficult. But we say that the logistic regression model and optimal tree had the lower sensitivity, possibly due to the fact that numbers of the individuals in both two groups were not equal and clinical risk factors were not considered in this study. Classification tree method gives more information with detail on diagnosis by evaluating a lot of risk factors together than logistic regression model. But making correct selection through constructed tree structures is very important to increase the success of results and to reach information which can provide appropriate explanations. (C) 2006 Elsevier Ltd. All rights reserved.