Machine learning based risk prediction models for oral squamous cell carcinoma using salivary biomarkers

Yi Cheng Wang, Pei Chun Hsueh, Chih Ching Wu, Yi Ju Tseng*

*此作品的通信作者

研究成果: Chapter同行評審

2 引文 斯高帕斯(Scopus)

摘要

Tumor-associated autoantibodies can be used as biomarkers for detecting different types of cancers. Our objective was to use machine learning techniques to predict high-risk cases of oral squamous cell carcinoma (OSCC) with salivary autoantibodies. The optimal model was using eXtreme Gradient Boosting (XGBoost) with the area under the receiver operating characteristic curve (AUC) of 0.765 (p < 0.01). Thus, applying machine learning model to early detect high-risk cases of OSCC could assist the clinic treatment and prognosis.

原文English
主出版物標題Public Health and Informatics
主出版物子標題Proceedings of MIE 2021
發行者IOS Press
頁面498-499
頁數2
ISBN(電子)9781643681856
ISBN(列印)9781643681849
DOIs
出版狀態Published - 1 7月 2021

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