Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran
نویسندگان
چکیده
منابع مشابه
data mining rules and classification methods in insurance: the case of collision insurance
assigning premium to the insurance contract in iran mostly has based on some old rules have been authorized by government, in such a situation predicting premium by analyzing database and it’s characteristics will be definitely such a big mistake. therefore the most beneficial information one can gathered from these data is the amount of loss happens during one contract to predicting insurance ...
15 صفحه اولReal-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran
OBJECTIVES Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-m...
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با توجه به گسترش روز افزون تقلب در حوزه بیمه به خصوص در بخش بیمه اتومبیل و تبعات منفی آن برای شرکت های بیمه، به کارگیری روش های مناسب و کارآمد به منظور شناسایی و کشف تقلب در این حوزه امری ضروری است. درک الگوی موجود در داده های مربوط به مطالبات گزارش شده گذشته می تواند در کشف واقعی یا غیرواقعی بودن ادعای خسارت، مفید باشد. یکی از متداول ترین و پرکاربردترین راه های کشف الگوی داده ها استفاده از ر...
Comparison of the efficiency of data mining methods in predicting type 2 diabetes
Background: Diabetes mellitus as a chronic disease is the most common disease caused by metabolic disorders and it is one of the most important health issues all around the world. Nowadays, data mining methods are applied in different fields of sciences due to data mining methods capability. Therefore, in this study, we compared the efficiency of data mining methods in predicting type 2 diabete...
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ژورنال
عنوان ژورنال: Healthcare Informatics Research
سال: 2013
ISSN: 2093-3681,2093-369X
DOI: 10.4258/hir.2013.19.3.177