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\begin{document}

\title{Machine Learning in Clinical Diagnosis of Head and Neck Cancer}



\author[1]{Hollie Black}%
\author[2]{David Young}%
\author[3]{Alexander Rogers}%
\author[3]{Jenny Montgomery}%
\affil[1]{University of Strathclyde Naval Architecture Ocean and Marine Engineering}%
\affil[2]{University of Strathclyde Department of Mathematics and Statistics}%
\affil[3]{Queen Elizabeth University Hospital}%


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  \date{May 25, 2024}


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\begin{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
logistic regression-based models with a penalty term worked best on the
data, ridge achieving an AUC of 0.7081. The demographic features
describing living alone and recreational drug use history 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.%
\end{abstract}%



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