PREDICTING DENTAL CARIES : A MACHINE LEARNING PROJECT

COMPUTER SCIENCE AND ENGINEERING

The prevalence of dental caries remains a significant concern in oral healthcare, necessitating the development of advanced predictive models for early detection and intervention. This study addresses the existing technological gaps by leveraging machine learning algorithms to enhance dental caries prediction accuracy. The aim is to mitigate the shortcomings of current diagnostic methodologies and improve overall oral health outcomes.The primary objective of this research is to develop a machine learning algorithm capable of precisely predicting dental caries, thereby facilitating early detection and intervention. Through the utilization of convolutional neural networks (CNN) or k-nearest neighbors (KNN), the study seeks to optimize healthcare interventions and improve patient outcomes. The methodology involves analyzing comprehensive datasets comprising questionnaire responses, oral examinations, and biological testing results to train and validate the predictive model. Key findings from the study include significant improvements in prediction accuracy achieved through the implementation of advanced machine learning techniques. Results indicate a notable reduction in false positives and false negatives, leading to enhanced diagnostic capabilities. The discussion highlights the implications of these findings for clinical practice, emphasizing the potential for timely interventions and cost-effective healthcare delivery. In conclusion, this research underscores the importance of leveraging machine learning in dental caries prediction to optimize healthcare interventions and improve patient outcomes. By addressing existing technological gaps and enhancing prediction accuracy, the study contributes to advancements in oral healthcare delivery.

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