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It is a pleasure to accept your manuscript entitled "Application of artificial intelligence and machine learning for prediction of oral cancer risk." in its current form for publication in the Journal of Oral Pathology and Medicine. The results demonstrate that the artificial neural network could perform well in estimating the probability of malignancy and improve the positive predictive value that could help to predict the individuals' risk of developing OC based on knowledge of their risk factors, systemic medical conditions, and clinic-pathological data. The accuracy of ANN for oral cancer prediction was 78.95% (95% CI, 54.43-931.95).Conclusion: Our results suggest that this machine-learning technique has the potential to help in oral cancer screening and diagnosis based on the datasets. Our analysis displayed that the average sensitivity and specificity of ANN for oral cancer prediction based on the 10-fold cross-validation analysis was 85.71% (95% confidence interval, 57.19-98.22) and 60.00% (95% CI, 14.66-94.73), respectively. Thirty-seven were female, and 36 were male, with a mean age of 63.09 years. Twenty-two (30.13%) were benign cases, and 51 (69.86%) were malignant cases. All records and observations were reviewed by Board-certified oral pathologist.Results: A total of 73 patients met the eligibility criteria. The dataset was randomly split into the training dataset 54 (75%) cases and testing dataset 19 (25%) cases. A total of 29 variables that were associated with the patients were used for developing the model. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on risk factors, systematic medical condition, and clinic-pathological features.Methods: A popular data mining algorithm artificial neural network was used for developing the artificial intelligence-based prediction model. Background: Oral cancer requires early diagnosis and treatment to increase the chances of survival.
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