The High Potential Prognostic Factors for Oral Squamous Cell Carcinoma (OSCC): A Neural Network (RBF) and Principle Components Analysis (PCA) Plot Approach
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Abstract
The aim of this research paper is to discuss the basic idea of the methodology building of the prognostic model for Oral Squamous Cell Carcinoma (OSCC) through the Radial Basis Function (RBF) model and Principle Component Analysis (PCA). This combined methodology is our main focus for the OSCC factor determination. Results from the RBF model will be verifying through PCA for validation purposes. Through the PCA plots, special characteristics or patterns of the tumor size will be assessed. The gained pattern will be studied for the characteristic association. This combined technique had led to successful research, giving the best results for decision making especially among the decision-maker and provide very important information for health science education. The utmost finding from this study, it provides a very useful information about the tumor size and their association and also gives an idea for further active treatment.
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References
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