Abstract:
Precursor lesions, pre-cancer, intra-epithelial neoplasia, and pre-malignant,
are all terms used to refer to Oral Potential Malignant Disorder (OPMD) in the
literature to characterize clinical manifestations that have the possibility to
develop into Oral Cancer. This is the most common malignancy among Sri
Lankan men and has the utmost death rate of all types of malignancies. The
main problem with this OPMD which is known as a pre-cancer stage is the
risk factors of it could vary according to the region and country because of its
nature. Hence, this research will expose the risk factors of OPMD in Sri Lanka,
the impact of risk factors, and the transformative potential of OPMD. Around
1000 data samples available at the Faculty of Dental Science, University
Peradeniya was collected, preprocessed, and analyzed by handling null values.
The Inter Quartile Range method is used for the outlier handling for this
identification. Machine learning techniques, such as Random Forest
classification, Support Vector Machine, Logistic Regression, and Variance
Analysis were used for the identification of risk factors. Moreover, Random
Forest, Support Vector Machine, Decision Tree, Logistic Regression, K
Nearest Neighbors, Gradient Boosting Tree, and XGP classification
algorithms were used to predict the Malignant Transformation Potential. The
most important risk factors, the habits of patients and medical history
attributes were identified, and the Gradient Boost Classifier, Logistic
Regression, and K Nearest Neighbors were identified as best models to predict
transformation potential with 97% of accuracy for each.
