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http://dx.doi.org/10.7236/IJIBC.2020.12.3.102

A research on the key factors for classification of diabetes based on random forest  

Shin, Yong sub (Graduate School of Smart Convergence Kwangwoon University)
Lee, Namju (Department of Physical Education, Institute of Information Technology, Kwangwoon University)
Hwang, Chigon (Department of Computer Engineering, Institute of Information Technology, Kwangwoon University)
Publication Information
International Journal of Internet, Broadcasting and Communication / v.12, no.3, 2020 , pp. 102-107 More about this Journal
Abstract
Recently, the number of people visiting the hospital is increasing due to diabetes. According to the Korean Diabetes Association, statistically, 1 in 7 adults over the age of 30 are suffering from diabetes. As such, diabetes is one of the most common diseases among modern people. In this paper, in addition to blood sugar, which is widely used for diabetes awareness, BMI, which is known to be related to diabetes, triglycerides and cholesterol that cause various complications in diabetics it was studied using random forest techniques and decision trees known to be effective for classification. The importance of each element was confirmed using the results and characteristic importance derived using two techniques. Through this, we studied the diabetes-related relationship between BMI, triglyceride, and cholesterol as well as blood sugar, a factor that diabetic patients should pay much attention to.
Keywords
Decision Tree; Random Forest; Supervised Learning; Diabetes;
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