Machine learning in clinical diagnosis of head and neck cancer

Black, Hollie and Young, David and Rogers, Alexander and Montgomery, Jenny (2024) Machine learning in clinical diagnosis of head and neck cancer. Clinical Otolaryngology. pp. 1-8. ISSN 1749-4478 (https://doi.org/10.1111/coa.14220)

[thumbnail of Black-etal-CO-2024-Machine-learning-in-clinical-diagnosis-of-head-and-neck-cancer]
Preview
 Text. Filename: Black-etal-CO-2024-Machine-learning-in-clinical-diagnosis-of-head-and-neck-cancer.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (211kB)| Preview

Abstract

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.

ORCID iDs

Black, Hollie ORCID logoORCID: https://orcid.org/0009-0002-5099-9302, Young, David ORCID logoORCID: https://orcid.org/0000-0002-3652-0513, Rogers, Alexander and Montgomery, Jenny;
CORE (COnnecting REpositories)