以時間合併演算及統計法處理多重時間序列資料與多重資料分類

Abstract

至2012年為止，癌症位居國人死因第一位已經31年了，其中肝癌比例更是居高不下；慢性肝病及肝硬化也位居十大死因之一，肝病如此嚴重的情況下,治療方法便相當重要。射頻燒灼術是治療肝癌的一種方法，近年來，更是越趨重要。對於那些因為肝癌而接受射頻燒灼術治療的人們，我們收集其就診的臨床檢驗資料，作為預測射頻燒灼術後肝癌復發與否的模型。這些龐大的臨床資料，先以不同的時間序列做整理，而後再利用時間摘要法將資料內容進一步轉化，於是在不同的時間序列內，就會有不同內容的時間摘要。此篇論文的目標在於探討不同時間序列內時間摘要法對於發展預測模型的成效。我們將原始資料統整為原始值、時間摘要法處理後，以及原始+時間摘要法雙處理值3類，並利用支持向量機作為分類發展預測模型的機器。結果顯示，除了不同的時間序列能有不同的統計結果外，時間摘要法也有效果，時間摘要法處理後的值，以及原始+時間摘要法雙處理值兩類對敏感度及特異度的提升有幫助。

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.