International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Head and neck cancer ranks among the most common and deadly forms of cancer globally. Detecting the disease at an early stage is essential for increasing survival rates and improving treatment success. Conventional diagnostic approaches typically depend on manual examinations and invasive testing procedures, which can contribute to delayed identification and higher mortality rates. This study introduces a machine learning–driven predictive framework designed for the early detection of head and neck cancer using clinical information. The model incorporates demographic characteristics, lifestyle habits, prior medical conditions, and reported symptoms to build an automated and effective prediction system. To enhance performance and minimize irrelevant data, the dataset undergoes preprocessing steps such as data cleansing, normalization, and feature selection. Several machine learning techniques—including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours—are applied and compared to determine the most accurate classification model. Evaluation is carried out using standard performance indicators, including accuracy, precision, recall, and F1-score. The findings reveal that ensemble learning methods outperform traditional classification algorithms in predictive capability, demonstrating their effectiveness for medical diagnostic tasks. The developed system is intended to support healthcare practitioners in making timely clinical decisions, lowering diagnostic inaccuracies, and improving overall efficiency. Ultimately, this research highlights the promise of machine learning in creating dependable, non-invasive, and affordable solutions for head and neck cancer prediction, contributing to enhanced patient outcomes and smarter healthcare systems.

Downloads: | DOI: 10.17148/IJARCCE.2026.15210