Risk factor determinants and comparison of supervised machine learning algorithms to predict head and neck cancer

Abstract

Head and Neck Cancer (HNC) has emerged as a major public health concern in India. The Indian state Bihar, which ranks fourth in the total number of new cancer cases, head and neck cancer is the second most common cancer and has an increasing trend. The application of machine learning (ML) in disease diagnosis is increasing gradually. With this background, an attempt has been made to determine the risk factors and compare the performance of different variants of supervised ML algorithms for HNC prediction. The study confirms that poor oral hygiene, tobacco, alcohol and human papilloma virus (HPV) infections are the significant risk factor for HNC occurrence in Bihar. In comparison to all the variants of supervised machine learning algorithm, Random Forest showed maximum accuracy. This study will beneficial in medical decision support systems.