基於機器學習演算法的射頻消融術後肝癌復發預測模型

Abstract

肝細胞癌在所有癌症的死亡率中常年位居前列，病患即使有精確診斷出肝癌腫瘤並接受有效治療，術後仍有高機率會復發。因此，透過分析病患術前的檢驗報告並建立復發預測模型，可以幫助進行術後追蹤，以期及早發現腫瘤的復發並加以治療。 本篇論文所使用的資料集是於2007~2013年，接受腫瘤射頻消融術作為第一次肝癌治療的病患。資料樣本數為334筆，其中256筆為術後一年後未復發的病患，78筆為復發的病患。這份資料集取自台大醫院資料庫，曾由一團隊進行研究分析，並發表了數篇研究成果，包含了資料庫建立、特徵提取，以及資料缺值插補等主題，雖然其中有提出復發預測模型的建立，不過僅有使用支援向量機，並未提及不同模型間效能的比較。 本篇論文聚焦於討論支援向量機、隨機森林和深度神經網路在各種實驗環境下的效能，包含了使用不同模型參數、資料標準化，以及資料升採樣的設置。

Mortality rate of hepatocellular carcinoma (HCC) has been one of the top among all kinds of cancers. Despite receiving accurate diagnosis and effective treatments, the recurrence rate of HCC is still high. Therefore, building recursive model by analyzing preoperative reports is helpful for follow-up and observing recurrence of tumor as soon as possible. The dataset used in this thesis are patients who received radiofrequency ablation (RFA) as first treatment. The size of the dataset is 334. 256 patients did not have recurrent HCC one year after RFA treatment and the other 78 patients had HCC. This dataset were collected from National Taiwan University Hospital (NTUH). One group in NTUH have done research on this dataset and proposed several papers, including database establishment, feature extraction, and data imputation. Although some of them have proposed an approach for building predictive model, the authors only used support vector machine and did not mention the comparison of performances between different models. This thesis is focusing on discussion between the performances of support vector machine, random forest, and deep neural network under a variety of experimental environment, including using different parameters of model, data normalizations, and methods of data up-sampling.