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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 歐陽彥正(Yen-Jen Oyang) | |
dc.contributor.author | Jui-Hsuan Chang | en |
dc.contributor.author | 張芮瑄 | zh_TW |
dc.date.accessioned | 2021-06-17T07:07:26Z | - |
dc.date.available | 2022-07-25 | |
dc.date.copyright | 2019-07-25 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-07-24 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72830 | - |
dc.description.abstract | 2015年於台灣台南市所爆發的登革熱疫情,主要流行的登革熱為新血清型的第二型病毒,市民多對此無免疫力。透過發現不同群住院病患的特徵可以協助醫生使得患者提早發現嚴重性,並藉此降低死亡率。本研究提出了一個創新的分群方法,能框出主要的群集核心,利用住院病人之血液檢測將其分群,並找到在不同群下適當的重症指標,使其有更高的勝算比。結果顯示,兩群住院病人的主要特徵一是有明顯高燒症狀但血小板值正常,二是無明顯發燒症狀但血小板值過低。第一群人中白血球指標大於6.81時,有3.58的勝算比,第二群人中白血球指標大於7.65時,有11.18的勝算比。總體而言,KDE分群後的結果發現第二群裡之患者更為嚴重。當該組病人使用較高的WBC作為閾值時,勝算比較分群前大大提高。 | zh_TW |
dc.description.abstract | In 2015, due to a new serotype dengue fever outbreak in Tainan City, the public was not immunized from it. Discovering the characteristics of different groups in hospitalized patients can help them to identify the high-risk groups timely and thereby reduce the case fatality rate. In this study, a novel KDE clustering was proposed to extract the main cluster cores and was utilized it to cluster inpatient into two groups by their blood tests. The results show that one group of inpatients has obviously high fever with normal platelet value, while the other group have no obvious fever but significant low platelet value. Furthermore, this study shows that if the white blood cell in the first group of people is greater than 6.81, there is an odds ratio of 3.58 for severity. If the white blood cell in the second group is greater than 7.65, there is an odds ratio of 11.18 for severity. Compare with the odds ratio of 4.8 before clustering, the results after KDE clustering find a more serious second group of patients. When this group uses a higher WBC as cutoff value, the odds ratio is greatly improved, and the results created by KDE clustering have highest odds ratio than other conventional clustering methods. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T07:07:26Z (GMT). No. of bitstreams: 1 ntu-108-R06h41010-1.pdf: 1710944 bytes, checksum: a7b394eca95ab5675778c6dbf053ad65 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 致謝 II
摘要 III Abstract IV Table of Contents V List of Figures VII List of Tables VIII Chapter 1 Introduction 1 1.1 Background 1 1.2 Research purpose and motivation 2 Chapter 2 Materials 4 Chapter 3 Related Works 6 3.1 k-means Clustering 7 3.2 EM Clustering 8 3.3 Density Estimation and Clustering 9 3.4 Principal component analysis 11 3.5 Linear discriminant analysis 12 Chapter 4 Methods 14 4.1 Kernel Density Estimation 14 4.2 Bandwidth selection 15 4.3 Threshold selection 17 4.4 KDE Clustering 18 4.5 Example 20 Chapter 5 Results and Discussion 23 5.1 Descriptive Statistics 23 5.2 KDE-Clustering on dengue database 24 5.3 Difference between severe and non-severe in each cluster 27 5.4 Comparison of Other Clustering Methods 33 Chapter 6 Conclusion and Future Works 45 References 47 | |
dc.language.iso | en | |
dc.title | 使用分群方法分析登革熱資料 | zh_TW |
dc.title | Analysis of dengue data with clustering | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 韓謝忱(Hsieh-Chen Han),王榮德(Jung-Der Wang),金傳春(Chwan-Chuen King) | |
dc.subject.keyword | 登革熱,核密度估計,集群分群, | zh_TW |
dc.subject.keyword | Dengue fever,Kernel density estimation,clustering analysis, | en |
dc.relation.page | 49 | |
dc.identifier.doi | 10.6342/NTU201901676 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-07-24 | |
dc.contributor.author-college | 共同教育中心 | zh_TW |
dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
顯示於系所單位: | 統計碩士學位學程 |
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