Predictive Analysis of Oral Cancer Using Lifestyle and Clinical Indicators
Abstract
Oral cancer remains a significant global health burden, especially in developing countries. Early detection through predictive analytics can dramatically improve outcomes. This study utilizes a comprehensive dataset of 84,922 records, incorporating demographic, lifestyle, and clinical data to predict oral cancer diagnoses. Using machine learning techniques, we analyze the relationship between various risk factors and oral cancer. The results highlight the importance of lifestyle and early screening in disease prediction and prevention.
Keywords
Download Options
Introduction
Oral cancer accounts for a considerable percentage of cancer-related morbidity worldwide. Traditional diagnostic approaches often detect the disease at advanced stages, reducing survival rates. Predictive modeling using machine learning can facilitate early detection based on patient data, offering a cost-effective solution to enhance public health interventions.
Conclusion
The results indicate that machine learning can successfully predict oral cancer from lifestyle and clinical data. Early diagnosis using such models can support healthcare systems in focusing efforts on at-risk populations. Future work will include real-time clinical deployment and expanding features such as genetic markers.