Harnessing Supervised Machine Learning for Early Detection of Oral Cancer: A Step Towards Smarter Healthcare
DOI:
https://doi.org/10.52783/jns.v14.1620Keywords:
Artificial Intelligence, Convolutional Neural Networks, Diagnosis, Early Detection, Machine Learning, Medical Imaging, Oral Cancer, Precision-Recall, Risk Factors, Supervised Learning, Support Vector Machine, Tumor DetectionAbstract
In order to improve patient outcomes in oncology, this study investigates the incorporation of supervised machine learning (ML) approaches to improve oral cancer early detection. Early detection of precancerous and cancerous lesions is crucial since oral squamous cell carcinoma accounts for a sizable percentage of cancers globally. Using clinical data and histopathology pictures, the study assesses many supervised learning models, concentrating on performance parameters including accuracy, sensitivity, specificity, and area under the curve (AUC). The study intends to determine the efficacy of these models in comparison to traditional diagnostic techniques by examining the body of existing literature and applying state-of-the-art machine learning algorithms. According to preliminary research, machine learning technology can greatly increase the precision of oral cancer screenings, which would enhance diagnosis and treatment planning. This study demonstrates how artificial intelligence (AI) is revolutionising healthcare and opening the door to more intelligent and effective diagnostic techniques in the fight against oral cancer.
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