An Artificial Intelligence Based Mortality in Head and Neck Disease Patients with Connected with smoking and Clinical Information
Dr Vani N, Associate Professor Computer Science and engineering B G S Institute of Technology
Adichunchanagiri University
BG Nagara, Karnataka
Swathi B
Computer Science and engineering
B G S Institute of Technology
Adichunchanagiri University BG Nagara, Karnataka
Abstract— Head and neck tumors address a huge worldwide well-being problem, influencing fundamental districts like the mouth, throat, and tongue. This study brings an original examination concerning the unpredictable exchange between the way of life factors, including smoking and human papillomavirus infection (HPV) inspiration, and the improvement of these diseases. Utilizing fundamental malignant growth credits like the cancer hub metastasis (TNM) grouping framework and HPV status, our exploration embraces a high-level computational methodology incorporating eight AI and four profound learning hyper-boundary tuned models to foresee death rates related to head and neck diseases. Surprisingly, our outcomes exhibit the greatest exactness of 98.8% accomplished by the slope-supporting calculation, highlighting its viability in mortality expectation. Besides, we recognize the term continuation from conclusion to the previous contact date as the most persuasive component, with a meaning of 40.8% in mortality expectation. Quantitative investigation using the region under the recipient's working trademark bend verifies the vigorous presentation of our classifiers, with a most extreme worth of 0.99 achieved by slope helping. These discoveries hold significant ramifications for clinical work, offering clinical experts important bits of knowledge into mortality expectation and directing the conveyance of custom-fit therapy
systems to work on understanding results in head and neck disease across the board.
Keywords— mortality prediction,human papilloma Virus (HPV),tumor-node-matastasis(TNM)
