Abstract
This study, for the first time, explores the integration of data science and machine learning for the classification and prediction of coronary artery calcium (CAC) scores. It focuses on tooth loss and patient characteristics as key input features to enhance the accuracy of classifying CAC scores into tertiles and predicting their values. Advanced analytical techniques were employed to assess the effectiveness of tooth loss and patient characteristics in the classification and prediction of CAC scores. The study utilized data science and machine learning methodologies to analyze the relationships between these input features and CAC scores. The research evaluated the individual and combined contributions of patient characteristics and tooth loss on the accuracy of identifying individuals at higher risk of cardiovascular issues related to CAC. The findings indicated that patient characteristics were particularly effective for tertile classification of CAC scores, achieving a classification accuracy of 75%. Tooth loss alone provided more accurate predicted CAC scores with the smallest average mean squared error of regression and with a classification accuracy of 71%. The combination of patient characteristics and tooth loss demonstrated improved accuracy in identifying individuals at higher risk with the best sensitivity rate of 92% over patient characteristics (85%) and tooth loss (88%). The results highlight the significance of both oral health indicators and patient characteristics in predictive modeling and classification tasks for CAC scores. By integrating data science and machine learning techniques, the research provides a foundation for further exploration of the connections between oral health, patient characteristics, and cardiovascular outcomes, emphasizing their importance in advancing the accuracy of CAC score classification and prediction.