A Predictive Model for Early Detection of Oral Cancer via Adaboost Classification Technique

Abstract

Oral cancer is a serious and potentially life-threatening disease that affects millions of people around the world. Early detection is critical for improving treatment outcomes and reducing mortality rates. This study aims to develop a predictive model for early detection of oral cancer using the AdaBoost classification technique. A dataset containing various risk factors associated with oral cancer was used to train and test the model. The results show that the AdaBoost algorithm was able to accurately classify oral cancer patients and non-cancer patients with high precision and recall rates. The developed predictive model could be used as a tool for early detection of oral cancer, thus improving patient outcomes and reducing mortality rates. The significance of oral cancer prediction and classification by improvised AdaBoost proposed technique produce potential to improve patient outcomes, facilitate early detection and treatment of oral cancer, and increase the efficiency and accuracy of the diagnostic process.