International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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

Author(s) K Padmavathi, C Deepa
Country India
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.
Keywords AdaBoost algorithm, Classification, Early detection, Improvised AdaBoost technique, Oral Cancer, Oral Cancer Dataset
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 5, Issue 4, July-August 2023
Published On 2023-08-29
Cite This A Predictive Model for Early Detection of Oral Cancer via Adaboost Classification Technique - K Padmavathi, C Deepa - IJFMR Volume 5, Issue 4, July-August 2023. DOI 10.36948/ijfmr.2023.v05i04.5909
DOI https://doi.org/10.36948/ijfmr.2023.v05i04.5909
Short DOI https://doi.org/gsnrgm
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E-ISSN 2582-2160
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