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.

• Authors: Yi-Cheng Wang, Pei-Chun Hsueh, Chih-Ching Wu, Yi-Ju Tseng
• Journal: Studies in health technology and informatics (Stud Health Technol Inform) Vol. 281 Pg. 498-499 (May 27 2021) ISSN: 1879-8365 [Electronic] Netherlands
• PMID: 34042619 (Publication Type: Journal Article)

Topics

• Carcinoma, Squamous Cell
• Head and Neck Neoplasms
• Humans
• Machine Learning
• Mouth Neoplasms
• Squamous Cell Carcinoma of Head and Neck