Predicting the Effectiveness of Laser Therapy in Periodontal Diseases Using Machine Learning Models

Аннотация

This study evaluates the effectiveness of COPYRIGHT machine learning models in predicting the outcomes of © 2025 Original content from this work may be used under the terms laser therapy for periodontal diseases. Various of the creative commons attributes 4.0 License. algorithms, including Neural Networks, Gradient Boosting, Random Forest, and Support Vector Machine, were applied to a dataset containing clinical variables such as pocket depth and gingival inflammation. The Neural Network model achieved the highest predictive accuracy with an AUC-ROC score of 0.91, followed by Gradient Boosting at 0.90. These models outperformed traditional techniques, demonstrating that machine learning can accurately predict treatment success. The findings suggest that machine learning can aid clinicians in personalizing laser therapy, optimizing treatment, and improving patient outcomes. Further research with diverse datasets is recommended to refine these models. Keywords: Machine learning, laser therapy, periodontal diseases, predictive accuracy, AUC-ROC, Neural Networks, Gradient Boosting, treatment outcomes, clinical variables, personalized treatment.