The oral cancer risk of each individual is basically analyzed by testing squamous cell carcinoma (SCC) and epithelial dysplasia (ED).
Using the above tests, they can be determined to be positive or negative for oral cancer/OPMDs. And the same can be determined with the help of some histological tests as stated above.
In order to accomplish their studies, researchers analyzed the Visual oral examination (VOE) in 1467 participants who joined through a community based program by 3 renowned dentists.
During the study, each individual’s status for oral cancer risk was thoroughly examined. They first monitored the follow up status of those participants who tested negative in the state-linked electronic health records.
Afterwards, they studied different aspects of the individual like familial risk factors, lifestyle, habitual, demography, and other health records.
A Deep-Learning Method for Oral Cancer Risk Diagnosis
A number of features like histologic diagnoses and input features (n=40) were employed to analyze as many as 10 machine learning algorithms. Along with that they also applied with 80:20 train-test splitting to the data captured previously.
Finally, an internal validation was conducted by employing 20% unused data by mainly using McNemar’s test. The above technique also used for optimal model selection Performance metrics included recall, specificity, and F1-score.
Also Read: Fresh COVID Cases in India Dip to 11,739