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A Comparative Study of Machine Learning Regression Approach on Dental Caries Detection

Publication Type : Journal Article

Publisher : Elsevier

Source : Procedia Computer Science

Url :

Campus : Amaravati

School : School of Computing

Year : 2022

Abstract : Cavities are the most prevalent consequence of dental caries, an infectious condition that weakens the structure of the teeth and may be spread from person to person. Research on dental caries, which is considered one of the most widespread problems with oral health, has been conducted with the purpose of early diagnosis owing to the discomfort and expense of treatment. In recent years, artificial intelligence has been utilized to construct models for estimating the probability of dental caries. In the current research work, the Machine learning technique has been provided as the prominent solution in providing the prediction model to detect dental caries. This research work reports a few machine learning algorithms such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Naïve Bayes (NB) for providing a model for dental caries detection. Finally, the said algorithms are evaluated over the influencing parameters such as accuracy, precision, recall, F1-Score, and Mathews Correlation Coefficient (MCC). The empirical analysis shows that the DT provides a more accurate model with an accuracy level of 85.62%.

Cite this Research Publication : Kumar, C., & Singh, B. (2022). A Comparative Study of Machine Learning Regression Approach on Dental Caries Detection. Procedia Computer Science, 215, 519-528.

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