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:: Volume 16, Issue 1 (9-2011) ::
Andishe 2011, 16(1): 34-46 Back to browse issues page
Logic regression and its application in predicting diseases
Parvin Sarbakhsh, Yadollah Mehrabi Dr *, Ali Akbar Khadem Maboudi Dr, Farzad Hadaegh Dr
Shahid Beheshti University of Medical Science, School of Public Health
Abstract:   (15837 Views)
Regression is one of the most important statistical tools in data analysis and study of the relationship between predictive variables and the response variable. in most issues, regression models and decision tress only can show the main effects of predictor variables on the response and considering interactions between variables does not exceed of two way and ultimately three-way, due to complexity of such interactions. To consider such interactions in the regression models, instead of individual variables in the model, we can construct a combination of them and use this combination as a new independent variable into the model Logic regression is a generalized regression and classification method that in this model, predictive variables are Boolean combinations that are made of the original binary variables. Annealing algorithm is used to find such combinations and their coefficients. randomization test or “null model test” is an overall test for signal in the data.also, cross-validation test can be used to determine the size of the logic tree model with the best predictive capability. As an example, we applied Logic Regression to predict diabetes in TLGS study.
Keywords: Annaeling Algorithm, Boolean Logic, interaction effects, Logic Regression.
Full-Text [PDF 616 kb]   (3380 Downloads)    
Type of Study: Research | Subject: Special
Received: 2011/12/26 | Accepted: 2012/01/3 | Published: 2014/07/12
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Sarbakhsh P, Mehrabi Y, Khadem Maboudi A A, Hadaegh F. Logic regression and its application in predicting diseases . Andishe. 2011; 16 (1) :34-46
URL: http://andisheyeamari.irstat.ir/article-1-128-en.html


Volume 16, Issue 1 (9-2011) Back to browse issues page
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