| Summary: | 碩士 === 國立屏東科技大學 === 資訊管理系所 === 106 === Abstract
Student ID：N10556004
Title of thesis：Applying Data Mining Technology to Explore the Effectiveness of Postprandial Blood Glucose Control in Diabetic Patients
Total page：74
Name of Institute：Department of Management Information System, National
Pingtung University of Science and Technology
Date of Graduate：June, 2018 Degree Conferred：Master
Name of Student：Chun-Chuan Chung Advisor：Deng-Neng Chen
The Contents of Abstract in This Thesis：
The population of diabetes is in an increasing trend nowadays. The Ministry of Health and Welfare has announced that the diabetes is ranked fifth of ten leading causes of death from 2017. Diabetes is a chronic metabolic syndrome disease, and it can get more easily to cause severe disease. Diabetes will damage health and waste more medical resources by long term.With the development of information technology and data science, data mining techniques have been extensively applied in all kinds of fields. This study has analyzed the medical examination database of a Regional Hospital in Southern Taiwan and applied C4.5 decision tree classifier in WEKA 3.8.1 to identify 6 key factors and extract 9 rules for the effectiveness of postprandial blood glucose control in diabetic patients. This research has
revealed that the diabetic patients not receiving health education with different treatments will affect the postprandial blood glucose control. In order to enhance the credibility of the analysis results, we collected relevant medical literature and conducting an in-depth interview with a clinical medical expert for evaluation of the analysis results. The conclusions show that every result is similar to clinical experience. The contribution of this study is to apply data mining techniques to explore information that provide references and application for healthcare personnel. With the developed model of this study, doctors can provide better healthcare and treatment to diabetic patients by rules, and lower the huge medical burden.
Keywords: data mining, Diabetes, Decision Tree algorithm
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