Predicting the possibility of brucellosis using data mining techniques in Famenin cohort database in Hamadan University of Medical Sciences

Predicting the possibility of brucellosis using data mining techniques in Famenin cohort database in Hamadan University of Medical Sciences


Predicting the possibility of brucellosis using data mining techniques in Famenin cohort database in Hamadan University of Medical Sciences

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Predicting the possibility of brucellosis using data mining techniques in Famenin cohort database in Hamadan University of Medical Sciences

ارائه دهنده: Provider: Mona Khamei

اساتید راهنما: Supervisors: Dr.Hamidreza Dezfoulian

اساتید مشاور: Advisory Professors: Dr.Fariba Keramat

اساتید ممتحن یا داور: Examining professors or referees: Dr. Parvaneh Samoei and Dr. Nafiseh Soleimani

زمان و تاریخ ارائه: Time and date of presentation: 2022/03/12

مکان ارائه: Place of presentation: virtual

چکیده: Abstract: In the field of medicine today, data collection on various diseases is of great importance and medical centers collect data on patients for various purposes. Analyzing this information and obtaining useful results and patterns in relation to diseases is one of the most important goals of collecting this data. In contrast, the sheer volume of information and the resulting confusion is a problem that prevents significant results from being achieved. Therefore, data mining can be used to overcome this problem and obtain effective features to predict diseases. The incidence of malaria is high in Iran and in the western provinces of the country, including Hamedan. brucellosis is an endemic disease in Iran. The use of data mining techniques to create a predictive model for the risk of brucellosis can be very effective in early detection, treatment and reduction of the effects of this disease. The main purpose of this study is to provide a model to predict the likelihood of brucellosis and identify the factors affecting the disease. This dissertation examines the Famenin cohort database in Hamadan University of Medical Sciences. The collected data were analyzed using data mining software and different data mining models were applied using classification and clustering methods to create a prediction model. The results of this study can be used as a comprehensive, accurate and valid model to predict the risk of brucellosis

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