2021
DOI: 10.1007/978-981-16-2934-1_4
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A Review on Application of Machine Learning and Deep Learning Algorithms in Head and Neck Cancer Prediction and Prognosis

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Cited by 4 publications
(1 citation statement)
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“…Most notably, The Cancer Genome Atlas (TCGA) [5] has data on over 520 SCCHN samples using various omics platforms. Exploring the relationships of these different datasets with clinical outcomes is challenging, and artificial intelligence (AI)-based methods are a rapidly growing area in medicine and healthcare to aid understanding and interpretability of such data [9,10]. In SCCHN, support vector machines (SVM) were used to develop a model for predicting recurrence/metastasis using whole genome copy number alteration (CNA) data with an accuracy of ~80% [11], and combining machine-learning methods with targeted proteomics has also been used to identify a prognostic signature in oral cancer [12].…”
Section: Introductionmentioning
confidence: 99%
“…Most notably, The Cancer Genome Atlas (TCGA) [5] has data on over 520 SCCHN samples using various omics platforms. Exploring the relationships of these different datasets with clinical outcomes is challenging, and artificial intelligence (AI)-based methods are a rapidly growing area in medicine and healthcare to aid understanding and interpretability of such data [9,10]. In SCCHN, support vector machines (SVM) were used to develop a model for predicting recurrence/metastasis using whole genome copy number alteration (CNA) data with an accuracy of ~80% [11], and combining machine-learning methods with targeted proteomics has also been used to identify a prognostic signature in oral cancer [12].…”
Section: Introductionmentioning
confidence: 99%