The Risk Factors of Pre-diabetes to Predict by Data Mining — Take a Regional Hospital in Pingtung as an Example

碩士 === 國立屏東科技大學 === 資訊管理系所 === 103 === Along with socio-economic prosperity and lifestyle change, global prevalence of diabetes has been increasing every year. Diabetes also becomes one of the ten leading causes of death in our country. The complications include foot neuropathy which may need amputa...

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Main Authors: Yang, Sheng-Hsiung, 楊勝雄
Other Authors: Tsai, Cheng-Fa
Format: Others
Language:zh-TW
Published: 2015
Online Access:http://ndltd.ncl.edu.tw/handle/t86b49
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spelling ndltd-TW-103NPUS53960072019-05-15T22:33:36Z http://ndltd.ncl.edu.tw/handle/t86b49 The Risk Factors of Pre-diabetes to Predict by Data Mining — Take a Regional Hospital in Pingtung as an Example 運用資料探勘技術進行糖尿病前期危險因子分析—以屏東某區域醫院為例 Yang, Sheng-Hsiung 楊勝雄 碩士 國立屏東科技大學 資訊管理系所 103 Along with socio-economic prosperity and lifestyle change, global prevalence of diabetes has been increasing every year. Diabetes also becomes one of the ten leading causes of death in our country. The complications include foot neuropathy which may need amputation, and retinopathy which causes blindness and renal failure. The risk of heart disease and stroke also elevates. If the high-risk group of diabetes can be discovered early and be provided with appropriate diet and exercise suggestions early, we can avoid the patients suffering from diabetes. This study tried to use data mining techniques to study the report data of adult health exams in a regional hospital. The main purpose was to develop a set of pre-diabetes predictive model, and to discuss the risk factors of pre-diabetes. The research samples were extracted through simple K-means clustering algorithm, and then were analyzed by six classifiers (Decision trees, Logistic Regression, Multilayer perceptron, SMO, Naive Bayes and RBF Network). We compared these six prediction modes in order to find the best one. The research results showed: (1) the prediction mode analyzed by Logistic Regression had the best performance, and the accuracy was 99.8%, the sensitivity was 98.7%, the specificity was 99.9%, and the positive predictive value (PPV) was 99.1%; (2) this study found five risk factors of pre-diabetes, including systolic blood pressure, diastolic blood pressure, body mass index (BMI), cholesterol level and fasting blood glucose level; (3) with decision trees, this study arranged 11 rules which are practically useful and easily interpretable. Before diabetes develops, the patients should be provided with medical personnel diagnostic aids or individual disease prevention, in order to discover the disease early, receive proper diet and exercise suggestions early as well as reduce the risk factors, so that the patients will not suffer from diabetes. Keyword:Pre-diabetes, Predictive model, Risk factor, Data mining Tsai, Cheng-Fa 蔡正發 2015 學位論文 ; thesis 119 zh-TW
collection NDLTD
language zh-TW
format Others
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description 碩士 === 國立屏東科技大學 === 資訊管理系所 === 103 === Along with socio-economic prosperity and lifestyle change, global prevalence of diabetes has been increasing every year. Diabetes also becomes one of the ten leading causes of death in our country. The complications include foot neuropathy which may need amputation, and retinopathy which causes blindness and renal failure. The risk of heart disease and stroke also elevates. If the high-risk group of diabetes can be discovered early and be provided with appropriate diet and exercise suggestions early, we can avoid the patients suffering from diabetes. This study tried to use data mining techniques to study the report data of adult health exams in a regional hospital. The main purpose was to develop a set of pre-diabetes predictive model, and to discuss the risk factors of pre-diabetes. The research samples were extracted through simple K-means clustering algorithm, and then were analyzed by six classifiers (Decision trees, Logistic Regression, Multilayer perceptron, SMO, Naive Bayes and RBF Network). We compared these six prediction modes in order to find the best one. The research results showed: (1) the prediction mode analyzed by Logistic Regression had the best performance, and the accuracy was 99.8%, the sensitivity was 98.7%, the specificity was 99.9%, and the positive predictive value (PPV) was 99.1%; (2) this study found five risk factors of pre-diabetes, including systolic blood pressure, diastolic blood pressure, body mass index (BMI), cholesterol level and fasting blood glucose level; (3) with decision trees, this study arranged 11 rules which are practically useful and easily interpretable. Before diabetes develops, the patients should be provided with medical personnel diagnostic aids or individual disease prevention, in order to discover the disease early, receive proper diet and exercise suggestions early as well as reduce the risk factors, so that the patients will not suffer from diabetes. Keyword:Pre-diabetes, Predictive model, Risk factor, Data mining
author2 Tsai, Cheng-Fa
author_facet Tsai, Cheng-Fa
Yang, Sheng-Hsiung
楊勝雄
author Yang, Sheng-Hsiung
楊勝雄
spellingShingle Yang, Sheng-Hsiung
楊勝雄
The Risk Factors of Pre-diabetes to Predict by Data Mining — Take a Regional Hospital in Pingtung as an Example
author_sort Yang, Sheng-Hsiung
title The Risk Factors of Pre-diabetes to Predict by Data Mining — Take a Regional Hospital in Pingtung as an Example
title_short The Risk Factors of Pre-diabetes to Predict by Data Mining — Take a Regional Hospital in Pingtung as an Example
title_full The Risk Factors of Pre-diabetes to Predict by Data Mining — Take a Regional Hospital in Pingtung as an Example
title_fullStr The Risk Factors of Pre-diabetes to Predict by Data Mining — Take a Regional Hospital in Pingtung as an Example
title_full_unstemmed The Risk Factors of Pre-diabetes to Predict by Data Mining — Take a Regional Hospital in Pingtung as an Example
title_sort risk factors of pre-diabetes to predict by data mining — take a regional hospital in pingtung as an example
publishDate 2015
url http://ndltd.ncl.edu.tw/handle/t86b49
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