7929

7377

كارشناسي ارشد

صنايع

اصفهان: دانشگاه صنعتي اصفهان، دانشكده صنايع و سيستم ها

1391

ده،105ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

جمشيد پرويزيان

داود خليلي

17/6/92

مهدي بيجاري، علي همداني

مهندسي صنايع و سيستم ها

ID7377

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

105 Application of Data Mining in Analyzing Risk Factors on Blood Glucose Level and Type 2 Diabetes Using TLGS Data Saeede Eftekhari s eftekhari@in iut ac ir Date of Submission 2013 1 23 Department of System and Industrial Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisor Dr Jamshid Parvizian japa@cc iut ac ir Abstract The main goal of this thesis is to analyze risk factors on fasting blood glucose and type 2 Diabetes with using data of Tehran Lipid and Glucose Study TLGS Initial results of TLGS show the high rate of metabolic disorders such as Diabetes Diabetes is one of the costly diseases in addition one of the risk factors on cardiovascular diseases with effects on blood vessels In TLGS due to the practical limits simultaneous effects of variables such as nutrition physical activity demography anthropometric clinical examination medical records and drug consumption on blood glucose have not been analyzed In this thesis effect of all these variables on blood glucose are analyzed Therefore it is necessary to model relations between considered variables and blood glucose One of the best techniques to model complex systems in various fields such as healthcare is data mining In this thesis for analyzing relations between variables data mining models both supervised and unsupervised are applied Before modeling the data is cleaned and described using statistical tools Because of capabilities of Artificial Neural Networks ANNs in modeling nonlinear relations between variables they are widely used in this research Therefore a neural network is proposed to predict fasting blood glucose In addition Self Organizing Maps as an unsupervised model for analyzing relations between variables is employed The work also consists the sensitivity analysis of the predicting model To identify the main risk factors of Diabetes a multilayer perceptron and a Logit model are proposed to diagnose Diabetes Results of sensitivity analysis show that generally anthropometric variables waist hip wrist and Body Mass Index are found more important than others Classification models show that risk factors on Diabetes are age triglyceride waist pulse systolic plod pressure family history of diabetes consumption of lipid drugs diastolic blood pressure consumption of blood pressure drugs hip BMI sex history of cardiovascular disease and carbohydrates in nutritive diet Key words Data Mining Blood Glucose Level and Type 2 Diabetes Artificial Neural Networks Sensitivity Analysis PDF created with pdfFactory trial version www pdffactory com

جمشيد پرويزيان

داود خليلي

مهدي بيجاري، علي همداني