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Predicting dental implant failure using machine learning: comparative evaluation of Random Forest, gradient boosting,
Marija S Milic1, Vladimir S Todorovic2, Milan Vucetic2
1School of Dental Medicine, General and Oral Physiology Department, University of Belgrade, Belgrade, Serbia.
Computer Methods in Biomechanics and Biomedical Engineering
|March 18, 2026
Summary
Machine learning models can predict dental implant failure. Random Forest demonstrated the highest accuracy, identifying key factors like implant location and patient age for risk stratification in implant dentistry.
Area of Science:
- Biomedical Engineering
- Dental Science
- Data Science
Background:
- Dental implant failure is a significant concern.
- Factors influencing failure include anatomical, systemic, and procedural elements.
Purpose of the Study:
- To evaluate the predictive performance of machine learning models for dental implant failure.
- To identify key predictors of implant success using data-driven approaches.
Main Methods:
- Utilized an open-access dataset (Liu et al. 2018) with demographic, surgical, prosthetic, and systemic variables.
- Assessed logistic regression, Random Forest, and gradient boosting machine learning models.
- Performed feature importance analysis to identify critical predictive factors.
Main Results:
- Random Forest achieved the highest performance (accuracy 0.85, ROC-AUC 0.79, F1-score 0.92, recall 0.97).
- Gradient boosting also showed strong predictive capabilities.
- Key predictors identified include implant location, sinus augmentation, implant dimensions, and patient age.
Conclusions:
- Ensemble machine learning models show significant potential for clinical risk stratification in implant dentistry.
- Interpretable feature metrics enhance the understanding of dental implant failure predictors.
- This approach can aid in improving patient outcomes and treatment planning.
