Title: Longevity Recommendation for Root Canal Treatment Using Machine Learning
Abstract: Root canal therapy is a vital dental procedure for salvaging severely decayed or infected teeth, preserving them instead of extracting them, thus averting the risk of reinfection. Nonetheless, the prevalence of root canal treatment (RCT) failure is surprisingly high, potentially leading to painful abscesses and severe infections. This study delves into the multifaceted reasons behind RCT failures and employs support vector machine (SVM) technology to predict treatment longevity. The research dataset comprises 332 manual instances, subjected to rigorous 10-fold cross-validation for testing and accuracy assessment. SVM is employed to categorize failed RCT cases into distinct classes, such as broken instruments, periapical radiolucency, root fractures, vertical root fractures, pulp stones, adequate periodontal support, periapical abscesses, overfilled cavities, and perforated or underfilled cavities. By scrutinizing the interplay between these treatment-failure-causing factors, the system discerns their impact on treatment duration. Comparisons are made with other machine learning models, including logistic regression (LR) and the naïve Bayes classifier (NB), to pinpoint the root causes of RCT failure in terms of accuracy, sensitivity, and specificity. Interestingly, logistic regression emerges as the top-performing model, with an impressive 92.47% accuracy rate. This study investigates the causes of RCT failure and employs SVM to predict treatment longevity, offering crucial insights for addressing this common dental issue. This study's findings highlight the efficacy of logistic regression for identifying RCT failure causes, providing valuable guidance for improving dental procedures and patient outcomes.