Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System.

ABSTRACT: The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are required to slow disease development. Machine learning (ML) models can benefit clinicians in the early detection of dental cavities through efficient and cost-effective computer-aided diagnoses. This study proposed a more effective method for diagnosing dental caries by integrating the GINI and mRMR algorithms with the GBDT classifier. Because just a few clinical test features are required for the diagnosis, this strategy could save time and money when screening for dental caries. The proposed metho

SUBMITTER: Kang IA 

PROVIDER: S-EPMC9952690 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature