Artificial neural network design for modeling of mixed bivariate outcomes in medical research data

Sedehi, Morteza. and Mehrabi, Yadollah. and Kazemnejad, Anoshirvan. and Johari majd, Vahid. and Hadaegh, Farzad. (2011) Artificial neural network design for modeling of mixed bivariate outcomes in medical research data. Iranian Journal of Epidemiology, 6 (4).

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Abstract

Background & Objective: Mixed outcomes arise when, in a multivariate model, response variables measured on different scales such as binary and continuous. Artificial neural networks (ANN) can be used for modeling in situations where classic models have restricted application when some of their assumptions are not met. In this paper, we propose a method based on ANNs for modeling mixed binary and continuous outcomes. Methods: Univariate and bivariate models were evaluated based on two different sets of simulated data. The …

Item Type: Article
Uncontrolled Keywords: پاسخهاي آميخته - شبكه عصبي مصنوعي، مدلهاي دومتغيره، مطالعه قندوليپيد تهران
Subjects: QT physiology > physics.mathematics.engineering
Divisions: Faculty of Health > Department of Epidemiology
Depositing User: Users 1 not found.
Date Deposited: 03 Dec 2017 05:18
Last Modified: 06 Mar 2018 07:53
URI: http://eprints.skums.ac.ir/id/eprint/6499

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