RESUMEN
Background: Newer concept of root canal therapy is single-visit one rather than conventional multivisit therapy. Major complaint of patients after root canal therapy is mild or severe pain. Aim: This study aims to assess the prevalence of postoperative discomfort after root canal treatment conducted in both single and multiple visits. Materials and Methods: An experiment using a randomized controlled trial design was conducted, including a total of 80 participants. These individuals were then separated into two groups, with each group consisting of 40 participants. Group A had single-visit root canal therapy, whereas Group B received multivisit root canal treatment. The incidence of pain after therapy was evaluated and compared at four time points: 6 hours, 12 hours, 24 hours, and 48 hours after obturation. Results: The level of pain experienced by patients in Group B was notably greater in comparison with individuals in Group A. Nevertheless, there was no statistically significant difference in the level of pain reported by the patients 48 hours after treatment in either of the groups. Conclusion: There is no significant difference in the occurrence of discomfort after endodontic treatment conducted in either a single visit or many visits, as seen during a 48-hour period after obturation.
RESUMEN
Background &Objective: Carcinoma of the breast is one of the major issues causing death in women, especially in developing countries. Timely prediction, detection, diagnosis, and efficient therapies have become critical to reducing death rates. Increased use of artificial intelligence, machine, and deep learning techniques create more accurate and trustworthy models for predicting and detecting breast cancer. This study aims to examine the effectiveness of several machine and modern deep learning models for prediction and diagnosis of breast cancer. METHODS: This research compares traditional machine learning classification methods to innovative techniques that use deep learning models. Established usual classification models such as k-Nearest Neighbors (kNN), Gradient Boosting, Support Vector Machine (SVM), Neural Network, CN2 rule inducer, Naive Bayes, Stochastic Gradient Descent (SGD), and Tree, and deep learning models such as Neural Decision Forest and Multilayer Perceptron used. The investigation, which was carried out using the Orange and Python tools, evaluates their diagnostic effectiveness in breast cancer detection. The evaluation uses UCI's publicly accessible Wisconsin Diagnostic Data Set, enabling transparency and accessibility in the study approach. RESULT: The mean radius ranges from 6.981 to 28.110, while the mean texture runs from 9.71 to 39.28 in malignant and benign cases. Gradient boosting and CN2 rule inducer classifiers outperform SVM in accuracy and sensitivity, whereas SVM has the lowest accuracy and sensitivity at 88%. The CN2 rule inducer classifier achieves the greatest ROC curve score for benign and malignant breast cancer datasets, with an AUC score of 0.98%. MLP displays distinguish positive and negative classes, with a higher AUC-ROC of 0.9959. with accuracy of 96.49%, precision of 96.57%, recall of 96.49%, and an F1-Score of 96.50%. CONCLUSION: Among the most commonly used classifier models, CN2 rule and GB performed better than other models. However, MLP from deep learning produced the greatest overall performance.