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This article was originally published here
J Oral Pathol Med. 2020 Nov 21. doi: 10.1111/jop.13135. Online ahead of print.
BACKGROUND/AIM: Machine learning analyses of cancer outcomes for oral cancer remain sparse compared to other types of cancer like breast or lung. The purpose of the present study was to compare the performance of machine learning algorithms in the prediction of global, recurrence-free five-year survival in oral cancer patients based on clinical and histopathological data.
METHODS: Data was gathered retrospectively from 416 patients with oral squamous cell carcinoma. The dataset was divided into training and test dataset (75:25 split). Training performance of five machine learning algorithms (Logistic regression, K-nearest neighbours, Naïve Bayes, Decision tree and Random forest classifiers) for prediction was assessed by k-fold cross validation. Variables used in the machine learning models were age, sex, pain symptoms, grade of lesion, lymphovascular invasion, extracapsular extension, perineural invasion, bone invasion and type of treatment. Variable importance was assessed and model performance on the testing data was assessed using receiver operating characteristic curves, accuracy, sensitivity, specificity and F1 score.
RESULTS: The best performing model was the Decision tree classifier, followed by the Logistic Regression model (accuracy 76% and 60%, respectively). The Naïve Bayes model did …
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