首页|School of Dentistry Reports Findings in Machine Learning (Predicting the Risk of Maxillary Canine Impaction Based on Maxillary Measurements Using Supervised Mac hine Learning)
School of Dentistry Reports Findings in Machine Learning (Predicting the Risk of Maxillary Canine Impaction Based on Maxillary Measurements Using Supervised Mac hine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Curitiba, Braz il, by NewsRx journalists, research stated, “To predict palatally impacted maxil lary canines based on maxilla measurements through supervised machine learning t echniques.The maxilla images from 138 patients were analysed to investigate int ermolar width, interpremolar width, interpterygoid width, maxillary length, maxi llary width, nasal cavity width and nostril width, obtained through cone beam co mputed tomography scans.” The news reporters obtained a quote from the research from the School of Dentist ry, “The predictive models were built using the following machine learning algor ithms: Adaboost Classifier, Decision Tree, Gradient Boosting Classifier, K-Neare st Neighbours (KNN), Logistic Regression, Multilayer Perceptron Classifier (MLP) , Random Forest Classifier and Support Vector Machine (SVM). A 5-fold cross-vali dation approach was employed to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision and F1 Score were calculated for ea ch model, and ROC curves were constructed. The predictive model included four va riables (two dental and two skeletal measurements). The interpterygoid width and nostril width showed the largest effect sizes. The Gradient Boosting Classifier algorithm exhibited the best metrics, with AUC values ranging from 0.91 [CI95% = 0.74-0.98] for test data to 0.89 [CI95% = 0.86-0.94] for crossvalidation. The nos tril width variable demonstrated the highest importance across all tested algori thms. The use of maxillary measurements, through supervised machine learning tec hniques, is a promising method for predicting palatally impacted maxillary canin es.”
CuritibaBrazilSouth AmericaCyborgsEmerging TechnologiesMachine LearningRisk and Prevention
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