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^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	Public Health and Informatics
Subtitle of host publication	Proceedings of MIE 2021
Publisher	IOS Press
Pages	498-499
Number of pages	2
ISBN (Electronic)	9781643681856
ISBN (Print)	9781643681849
DOIs	https://doi.org/10.3233/SHTI210213
State	Published - 1 Jul 2021

Keywords

Autoantibody
Machine learning
Oral squamous cell carcinoma

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3233/SHTI210213

Cite this

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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.",

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AU - Wang, Yi Cheng

AU - Hsueh, Pei Chun

AU - Wu, Chih Ching

AU - Tseng, Yi Ju

PY - 2021/7/1

Y1 - 2021/7/1

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 - Chapter

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SN - 9781643681849

SP - 498

EP - 499

BT - Public Health and Informatics

PB - IOS Press

ER -

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

Abstract

Keywords

UN SDGs

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