Machine Learning in Clinical Diagnosis of Head and Neck Cancer.

OBJECTIVE

Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.

DESIGN

An observational cohort study.

SETTING

Queen Elizabeth University Hospital.

PARTICIPANTS

Patients who were referred via the USOC pathway between January 2019 and May 2021.

MAIN OUTCOME MEASURES: Predicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.

RESULTS

The classic statistical method of ordinal logistic regression worked best on the data, achieving an AUC of 0.6697 and balanced accuracy of 0.641. The demographic features describing recreational drug use history and living situation were the most important variables alongside the red flag symptom of a neck lump.

CONCLUSION

Further studies should aim to collect larger samples of malignant and pre-malignant patients to improve the class imbalance and increase the performance of the machine learning models.