Saeed Samadi; Minoo Nazifi Naeini; Sahar Abbaspour
Volume 8, Issue 7 , January and February 2012, , Pages 948-957
Abstract
Introduction: Using neural networks and genetic algorithms in evaluating health-related variables has increased recently. Employing intelligent tools for diagnosis and treatment of diseases can reduce medical errors and human and financial losses. In this paper, medical applications of ...
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Introduction: Using neural networks and genetic algorithms in evaluating health-related variables has increased recently. Employing intelligent tools for diagnosis and treatment of diseases can reduce medical errors and human and financial losses. In this paper, medical applications of neural networks have been studied in order to help both medical and artificial intelligence researchers. Methods: We used an existing sample in SPSS (patient_los.sav). The sample consisted of patients who received treatment for heart disease. Multilayer perceptron (MLP) was employed to build a neural network to predict the cost and length of treatment. Duration of hospitalization and treatment cost were considered as dependent variables. Other variables were entered into the model as agents or factors. Results: Neural networks can evaluate the outcomes of patients who have or have not undergone surgery. Separate networks can then be used to predict treatment and hospitalization costs and duration provided that the patients who had surgery had been identified. Conclusion: Neural networks designed in this paper can well forecast the usual outcomes of patients. Multilayer neural networks can precisely identify patients who would die after surgery. Non-linear properties of neural networks can help in modeling and forecasting.
Mostafa Emadzadeh; Saeed Samadi; Samira Paknezhad
Volume 8, Issue 3 , July and August 2011
Abstract
Introduction: Individuals and families' accessible incomes increase as a result of economic development and income inequality reduction. This in turn leads to health promotion in the community. So, this study mainly aimed to survey the effects of unequal income distribution on health in selected members ...
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Introduction: Individuals and families' accessible incomes increase as a result of economic development and income inequality reduction. This in turn leads to health promotion in the community. So, this study mainly aimed to survey the effects of unequal income distribution on health in selected members of Organization of Islamic Countries (OIC). Methods: This descriptive-analytic study collected the data and information from the Internet, library resources and journals. The model used included some variables, such as income level, income inequality, level of savings and education level. The study population consisted of 18 members of OIC between 1980 and 2005. The analysis was performed using panel data method and random coefficient model. The data was entered into Microsoft Excel software. Then, estimation was made by FLeamer test in Stata 9.2. Finally, Eviews3 was used to demonstrate the effects of variables and the difference between cross-sectional data. Results: Considering life expectancy as a health index, and keeping the per capita income constant, income inequality (measured by Gini coefficient) had a reverse effect on health only in 6 out of 18 countries (according to inequality income hypothesis). Since life expectancy could not show the health changes in countries with more income inequality, the product of life expectancy and per capita income was used as health index. With income level, savings and education levels as control variables, it was observed that education and income have significantly positive effects on health. Conclusion: In the effort to promote health, one must emphasize not only on primary care systems, but also on issues such as improving income inequality, because an improvement in income distribution leads to increased level of life among masses of people through health, nutritional and educational enhancements. Keywords: Income; Life Expectancy; Education.