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

資訊管理學系

研究成果: 雜誌貢獻 › 期刊論文 › 同行評審

1 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	???core.languages.en_GB???
頁（從 - 到）	498-499
頁數	2
期刊	Studies in Health Technology and Informatics
卷	281
DOIs	https://doi.org/10.3233/SHTI210213
出版狀態	已出版 - 27 5月 2021

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10.3233/SHTI210213

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title = "Machine Learning Based Risk Prediction Models for Oral Squamous Cell Carcinoma Using Salivary Biomarkers",

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.",

keywords = "autoantibody, machine learning, oral squamous cell carcinoma",

author = "Wang, {Yi Cheng} and Hsueh, {Pei Chun} and Wu, {Chih Ching} and Tseng, {Yi Ju}",

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T1 - Machine Learning Based Risk Prediction Models for Oral Squamous Cell Carcinoma Using Salivary Biomarkers

AU - Wang, Yi Cheng

AU - Hsueh, Pei Chun

AU - Wu, Chih Ching

AU - Tseng, Yi Ju

PY - 2021/5/27

Y1 - 2021/5/27

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.

KW - autoantibody

KW - machine learning

KW - oral squamous cell carcinoma

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U2 - 10.3233/SHTI210213

DO - 10.3233/SHTI210213

M3 - 期刊論文

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AN - SCOPUS:85107238073

SN - 0926-9630

VL - 281

SP - 498

EP - 499

JO - Studies in Health Technology and Informatics

JF - Studies in Health Technology and Informatics

ER -

Machine Learning Based Risk Prediction Models for Oral Squamous Cell Carcinoma Using Salivary Biomarkers

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