Irregularly Spaced Longitudinal Data Mining Using a Dynamic Period Slicing Algorithm
Date Issued
2015
Date
2015
Author(s)
Kuo, Chien-Han
Abstract
Radiofrequency ablation (RFA) is a common treatment for the hepatocellular carcinoma (HCC). Recurrence of HCC is an important issue despite effective treatments with tumor eradication. In this study, for those who had HCC and were treated by RFA, their clinical data are collected to build predictive models which can be used to predict the recurrence of HCC patients after RFA treatment. These clinical data are in the form of longitudinal data, which consists of static features and temporal features. We develop a data preprocessing method called Dynamic Period Slicing (DPS) combined with temporal abstraction (TA) to extract the high-level features from the original temporal features. We use DPS to divide a given time period into several partitions, then use quantitative TA to calculate the statistical measurements from the values in a given partition. After implementing the above two methods, we can obtain new meta-features, then combine the meta-features with original static features to form new processed data. Support Vector machine (SVM) was selected as the classifier to build predictive models with simulated annealing and random forest feature selection methods.
Subjects
Dynamic Period Slicing
Temporal Abstraction
RFA
Liver cancer
SVM
SDGs
Type
thesis
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