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Analysis of dengue data with clustering
Dengue fever,Kernel density estimation,clustering analysis,
|Publication Year :||2019|
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.
|Appears in Collections:||統計碩士學位學程|
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