Machine learning predicts oral cancer risk

25 Jun 2022
Machine learning predicts oral cancer risk

The Interactive Talk presentation, “Predicting Oral Cancer Risk using Machine Learning”, will take place on Saturday, June 25th, 2022 at 2 p.m. China Standard Time (UTC+08:00) during the “e-Oral Health Network I” session.

The study, undertaken by John Adeoye of the University of Hong Kong, SAR China, aims to develop a machine learning-based platform to predict the risk of oral cancer and oral potentially malignant disorders (OPMDs).

Visual oral examination (VOE) was performed among 1467 participants of a community-based screening program by three calibrated dentists prospectively.

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.

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-neighbours was implemented for class imbalance correction.

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.

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 organised screening.

Article: Machine Learning Predicts Oral Cancer Risk

Source: International Society for Dental Research