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

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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 languageEnglish
Title of host publicationPublic Health and Informatics
Subtitle of host publicationProceedings of MIE 2021
PublisherIOS Press
Pages498-499
Number of pages2
ISBN (Electronic)9781643681856
ISBN (Print)9781643681849
DOIs
StatePublished - 1 Jul 2021

Keywords

  • Autoantibody
  • Machine learning
  • Oral squamous cell carcinoma

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