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 |
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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 | |
State | Published - 1 Jul 2021 |
Keywords
- Autoantibody
- Machine learning
- Oral squamous cell carcinoma