Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis - Dey, Priya; Ogwo, Chukwuebuka; Tellez, Marisol (Frontiers in Oral Health, 2024)
I am writing to submit our manuscript entitled, "COMPARISON OF TRADITIONAL REGRESSION MODELING VS AI MODELING FOR THE PREDICTION OF DENTAL CARIES-A SECONDARY DATA ANALYSIS" for consideration in the special issue of "Responsible Artificial Intelligence and Machine Learning Methods for Equity in Oral Health" within the Frontiers Oral Health Journal. We conducted a study to predict childhood and permanent dental caries for participants aged 6-16 years utilizing newer statistical approaches such as machine learning (ML) approach. Our study found that ML approach was able to predict childhood dental caries with more accuracy, precision and less bias in comparison to traditional statistical tools. Given that there are limited studies being done utilizing newer statistical approaches for prediction of dental caries, we believe the findings of our study will appeal to clinicians, public health professionals and policy makers who have access to your journal. This is the first study to use machi