# Machine-Learning Applications in Oral Cancer: A Systematic Review
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Clinical** & **Applied** & **Input** & **n** & **ROB** & **Concluding** \\  & **Context** & **Algorithm** & **Features** & **Samples** & & **Remarks** \\ \hline
[17] & 2019 & Prognosis & XGBOST & Expression profiles and clinical data & 291 & + & A three-mRNA signature (CLEC3B, C6, and CLCN1) successfully predicted the survival of OSC patients \\ \hline
[18] & 2016 & Prognosis & GP, SVM, LR & Personal details, medical history, p53, p63 & 31 & + & Genetic programing (GP) an ideal prediction model for cancer clinical and genomic data \\ \hline
[19] & 2018 & Diagnosis and Prevention & SVM & miRNA expression & 122 & + & Using the platform with an ML algorithm, it discovers miRNA expression patterns capable of separating healthy subjects from OSCC patients \\ \hline
[20] & 2005 & \begin{tabular}{c} Potentially \\ malignant oral \\ lesions \\ (pre-cancer) \\ \end{tabular} & WNN & \
\begin{tabular}{c} TEM images of \\ collagen fibers \\ from oral \\ susceptiblenal \\ region \\ \end{tabular} & 145 & + &
\begin{tabular}{c} The trained network was able to classify \\ normal and oral pre-cancer stages \\ \end{tabular} \\ \hline
[21] & 2019 & \
\begin{tabular}{c} Potentially \\ malignant oral \\ lesions \\ (pre-cancer) \\ \end{tabular} & SVM, RF, LR, LDA, KNN & Cytology images & 60 & + &
\begin{tabular}{c} Applicability of tele-cytology for accurate, \\ remote diagnosis and use of automated \\ ANN-based analysis in improving its efficacy \\ \end{tabular} \\ \hline
[22] & 2015 & \begin{tabular}{c} Therapy and \\ quality of life \\ \end{tabular} & \
\begin{tabular}{c} CTREE, RF, \\ BA, SVM \\ \end{tabular} & Gene expression & 486 & + &
\begin{tabular}{c} Analyzed the dysregulated gene pairs \\ implemented an ensemble-based feature \\ selection approach to prioritize targets in oral \\ squamous cell carcinoma (OSCC) for \\ therapeutic exploration \\ \end{tabular} \\ \hline
[23] & 2018 & Prognosis & \begin{tabular}{c} KSTAR, IBK, \\ RFC, RT \\ \end{tabular} & \
\begin{tabular}{c} Personal details, \\ medical history, \\ smoking, betel nut \\ chewing, and \\ drinking \\ \end{tabular} & 1428 & + &
\begin{tabular}{c} Evidence-based diagnostic model using \\ machine-learning techniques for the \\ prediction of risk factors of recurrent \\ oral cancer \\ \end{tabular} \\ \hline
[24] & 2010 & \
\begin{tabular}{c} Diagnosis and \\ Prevention \\ \end{tabular} & LDA & Spectral data & 57 & + &
\begin{tabular}{c} Presents an approach to adaptively adjust \\ spectral window sizes for feature extraction \\ from optical spectra \\ \end{tabular} \\ \hline
[25] & 1998 & \begin{tabular}{c} Diagnosis and \\ Prevention \\ \end{tabular} & ANN & \
\begin{tabular}{c} Personal details, \\ dental attendance, \\ and smoking and \\ drinking habits \\ \end{tabular} & 2027 & + &
\begin{tabular}{c} Sensitivity analysis using a decision model to \\ simulate opportunistic screening for oral \\ cancer and pre-cancer \\ \end{tabular} \\ \hline
[26] & 2017 & Prognosis & \begin{tabular}{c} LDA, QDA, \\ RF, SVM \\ \end{tabular} & \
\begin{tabular}{c} Images of \\ H&&E-stained tissue \\ sections \\ \end{tabular} & 115 & + &
\begin{tabular}{c} Investigates computer-extracted image \\ features of nuclear shape and texture on \\ digitized images of H&E-stained tissue \\ sections for risk stratification of oral cavity \\ squamous cell carcinoma patients compared \\ with standard clinical and \\ pathologic parameters \\ \end{tabular} \\ \hline
[27] & 2011 & \
\begin{tabular}{c} Therapy and \\ quality of life \\ \end{tabular} & ANN & Speech recording & 51 & - &
\begin{tabular}{c} Applicability of neural network feature \\ analysis of nasalance in speech to assess \\ hypensality in speech of patients treated \\ for oral or oropharyngeal cancer \\ \end{tabular} \\ \hline
[28] & 2009 & \begin{tabular}{c} Potentially \\ malignant oral \\ lesions \\ (pre-cancer) \\ \end{tabular} & SVM & \
\begin{tabular}{c} Images of SECT \\ (sub-epithelial \\ connective tissue) \\ of NOM and OS \\ \end{tabular} & 20 & + &
\begin{tabular}{c} Automated classification method using SVM \\ for understanding the deviation of normal \\ structural profile of oral mucosa during \\ precancerous changes \\ \end{tabular} \\ \hline
[29] & 2020 & \begin{tabular}{c} Diagnosis and \\ Prevention \\ \end{tabular} & CNN & \
\begin{tabular}{c} Histopathological \\ images \\ \end{tabular} & 8321 & + &
\begin{tabular}{c} CNN-based multi-class grading method of \\ OSCC could be used for diagnosis of patients \\ with OSCC \\ \end{tabular} \\ \hline
[30] & 2011 & \begin{tabular}{c} Potentially \\ malignant oral \\ lesions (pre-cancer) \\ \end{tabular} & SVM & \
\begin{tabular}{c} Images of surface \\ epithelium from \\ oral mucosa \\ \end{tabular} & 158 & + &
\begin{tabular}{c} Classification based on textural features for \\ the development of a computer-assisted \\ screening of oral sub-mucous fibrosis (OSF) \\ \end{tabular} \\ \hline
[31] & 2020 & \begin{tabular}{c} Diagnosis and \\ Prevention \\ \end{tabular} & \begin{tabular}{c} DTREE, SVM, \\ KNN, LDA, \\ LR \\ \end{tabular} & \
\begin{tabular}{c} Histopathological \\ images \\ \end{tabular} & 720 & + &
\begin{tabular}{c} SVM and linear discriminant classifier gave \\ the best result for texture and color features, \\ respectively, from the histopathological \\ images \\ \end{tabular} \\ \hline
[32] & 2017 & \begin{tabular}{c} Diagnosis and \\ Prevention \\ \end{tabular} & CNN & \
\begin{tabular}{c} Images of confocal \\ analyzer \\ endomicroscopy \\ (CLE) \\ \end{tabular} & 7894 & + &
\begin{tabular}{c} Novel automatic approach for OSCC \\ diagnosis using deep-learning technologies \\ on CLE images \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}