Machine Learning Platform Helps Predict Oral Cancer Risk

Machine learning is a successful tool for predicting oral cancer risk and may be applied to identify ‘at-risk populations’.

A machine learning-based platform helps predict oral cancer risk. The platform also identifies Oral Potentially Malignant Disorders (OPMDs).

Visual Oral Examination (VOE) was performed among 1,467 participants of a community-based screening program by three calibrated dentists prospectively.

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The study demonstrated that machine learning is a successful tool for predicting oral cancer risk and may be applied to identify ‘at-risk populations’ in opportunistic and organized screening.

Each individual’s status was defined as positive/negative for oral cancer/OPMDs and histologic confirmation of Epithelial Dysplasia (ED) and Squamous Cell Carcinoma (SCC) was performed for positive status.

Follow-up status of those that screened negative was monitored via state-linked electronic health records. Information on demography, habitual, lifestyle and familial risk factors was obtained, and expired carbon monoxide levels (in ppm) were assessed using a monitor.

A Deep-Learning Method for Oral Cancer Diagnosis

Input features (n = 40) and histologic diagnoses were used to populate 12 machine learning algorithms with 80:20 train-test splitting applied to the data randomly during development.

Recursive feature elimination with 10-fold cross-validation was used for feature selection while synthetic-minority-oversampling-technique with edited-nearest-neighbors was implemented for class imbalance correction.

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Internal validation was conducted with the unused 20% data with the comparison of outputs using McNemar’s test used for optimal model selection Performance metrics included recall, specificity, and F1-score.

Source-Eurekalert

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