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

doi:10.3233/SHTI210213

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: 圖書/報告稿件的類型 › 章節 › 同行評審

4 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	英語
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	https://doi.org/10.3233/SHTI210213
出版狀態	已出版 - 01 07 2021

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© 2021 European Federation for Medical Informatics (EFMI) and IOS Press. All rights reserved.

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abstract = "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.",

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N2 - 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.

AB - 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.

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KW - Oral squamous cell carcinoma

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ER -

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

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