Machine Learning Based Risk Prediction Models for Oral Squamous Cell Carcinoma Using Salivary Biomarkers
Keyword(s):
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.
Keyword(s):
Saliva protein biomarkers to detect oral squamous cell carcinoma in a high-risk population in Taiwan
2016 ◽
Vol 113
(41)
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pp. 11549-11554
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Keyword(s):
Low Risk
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2020 ◽
2020 ◽
2020 ◽
2020 ◽