Multiple Time Series Data Processing for Classification with Period Merging Algorithm and Statistical Measures
Date Issued
2014
Date
2014
Author(s)
Su, Wei-Ti
Abstract
Cancer has been ranked first in the causes of death for 31 consecutive years in Taiwan. Radiofrequency ablation (RFA) is a treatment for hepatocellular carcinoma (HCC) and it becomes one of important therapies for HCC these years. For those who had HCC and were treated by RFA, their clinical data are collected to build predictive models which can be used in predicting the recurrence or not of liver cancer after RAF treatment. Clinical data with multiple measurements are merged based on different time periods and these data are further transformed based on temporal abstraction (TA). Data processed by TA reveal variations of clinical data with different time points. The goal of this study is to evaluate whether clinical data handled by TA could increase performance of predictive models. Different data sets are used in developing predictive models, including clinical data which are not processed by TA called the original data set, clinical data which are processed by TA called the TA data set, and combination of the original data set and the TA data set called the TA+original data set. Support vector machine (SVM) was selected as a classifier to develop predictive models. The results demonstrate data sets processed by TA provide benefit for predictive models.
Subjects
時間序列
時間摘要
肝癌
射頻燒灼術
預測模型
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-103-R01945046-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):e36b54cc08315613700f949afbec65ab