M Sedehi, Y Mehrabi, A Kazemnejad, V Joharimajd, F Hadaegh,
Volume 6, Issue 4 (3-2011)
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 scaled conjugate gradient (SCG) algorithm was used for optimization. To end the algorithm and finding optimum number of iteration and learning coefficient, mean squared error (MSE) was computed. Predictive accuracy rate criterion was employed for selection of appropriate model. We also used our model in medical data for joint prediction of metabolic syndrome (binary) and HOMA-IR (continues) in Tehran Lipid and Glucose Study (TLGS). The codes were written in R 2.9.0 and MATLAB 7.6.
Results: The predictive accuracy for univariate and bivariate models based on simulated dataset Ι, where two outcomes associated with a common covariate, were shown to be approximately similar. However, in simulated dataset ΙΙ in which two outcomes associated with different covariates, predictive accuracy in bivariate models were seen to be larger than that of univariate models.
Conclusions: It is indicated that the predictive accuracy gain is higher in bivariate model, when the outcomes share a different set of covariates with higher level of correlation between the outcomes.
Mh Mehrolhasani, V Yazdi Feyzabadi , N Oroomiei, R Seyfaddini , S Mirzaei,
Volume 13, Issue 0 (3-2018)
Abstract
Background and Objectives: Different governance approaches have various definitions and systems about health. The purpose of this study was to compare the appropriateness of the health system performance with the ideology of the selected countries.
Methods: In this comparative study, liberal countries (America, Canada, France), social countries (Russia, China, Cuba) and mixed countries (Sweden, Norway, England) were selected purposefully. Data were obtained from World Bank and WHO’s published documents and discourse literature studies. Causal layered analysis framework was used for data analysis.
Results: Comparison of health indicators showed that mixed countries were in a better position than the other two groups. The health system’s stewardship of the liberal, mixed, and social countries were decentralized, semi-centralized, and centralized, respectively. Discourses of the liberal states were based on the capitalist economy, with lack of reliance on natural resources. Socialist countries, a socialist economy system emphasizes the use of natural resources. In these countries governmental involvement is maximum. Mixed countries have a constitutional monarchy government and benefit from both of these approaches to create welfare based on the ideology of liberalism and the welfare state approach.
Conclusion: Mixed countries with appropriate economic- social conditions, semi-centralized structure of service delivery, suitable financing system, and regional and local management of services (highlighting the role of municipalities), have better health status than other countries. The ideology of the countries forms the social, economic, and political structures as well health. Iran should consider various layers of metaphor, discourse, casual structures, and litany for redesigning the health system.