fa مقایسه‌ی مدل‌های شبکه‌ی عصبی مصنوعی با رگرسیون لجستیک و تحلیل ممیزی در پیش‌بینی سندرم متابولیک پژوهشی Original <p class="Abstract" style="MARGIN: 0cm 1cm 0pt DIRECTION: ltr unicode-bidi: embed TEXT-ALIGN: justify"><strong><font face="Arial">Abstract</font></strong></p><p class="Abstract" style="MARGIN: 0cm 1cm 0pt DIRECTION: ltr unicode-bidi: embed TEXT-ALIGN: justify"><strong><font face="Arial"><u>Introduction</u>: Artificial neural networks as a modern modeling method have received considerable attention in recent years. The models are used in prediction and classification in situations where classic statistical models have restricted application when some, or all of their assumptions are met. This study is aimed to compare the ability of neural network models to discriminant analysis and logistic regression models in predicting the metabolic syndrome. <u>Materials & Methods</u>: A total of 347 participants from the cohort of the <place w:st="on"><city w:st="on">Tehran</city></place> Lipid and Glucose Study (TLGS) were studied. The subjects were free of metabolic syndrome at baseling according to the ATPIII criteria. Demographic characteristics, history of coronary artery disease, body mass <span style="LETTER-SPACING: -0.2pt">index, waist, LDL, HDL, total cholesterol, triglycerides, fasting and 2 hours blood sugar, smoking, systolic and diastolic blood pressure were measured at baseline. Incidence of metabolic syndrome after about 3 years of follow up was considered a dependent variable. Logistic regression, discriminant analysis and neural network models were fitted to the data. The ability of the models in predicting metabolic syndrome was compared using ROC analysis and the Kappa statistic, for which, MATLAB software was used. <u>Results</u>: The areas under receiver operating </span>characteristic (ROC) curve for logistic regression, discriminant analysis and artificial neural network models (15: 8: 1) and (15: 10: 10) were estimated as 0. 749, 0. 739, 0. 748 and 0. 890 respectively. Sensitivity of models were calculated as 0. 483, 0. 677, 0. 453 and 0. 863 and their specificity as 0. 857, 0. 660, 0. 910 and 0. 844 respectively. The Kappa statistics for these models were 0. 322, 0. 363, 0. 372 and 0. 712 respectively. <u>Conclusion</u>: Results of this study indicate that artificial neural network models perform better than classic statistical models in predicting the metabolic syndrome. </font></strong></p> مدل‌های شبکه‌ی عصبی مصنوعی، رگرسیون لجستیک، تحلیل ممیزی، سندرم‌ متابولیک Artificial Neural Network, Logistic Regression, Discriminant Analysis, Metabolic syndrome 638 646 http://ijem.sbmu.ac.ir/browse.php?a_code=A-10-799-4&slc_lang=fa&sid=1 مرتضی سدهی 4000319475328460011363 4000319475328460011363 No یداله محرابی ymehrabi@gmail.com 4000319475328460011461 4000319475328460011461 Yes انوشیروان کاظم نژاد 4000319475328460011462 4000319475328460011462 No فرزاد حدائق 4000319475328460011463 4000319475328460011463 No