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

This study evaluates the effectiveness of machine learning models in predicting the outcomes of laser therapy for periodontal diseases. Various 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.