Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics

Uğur Toprak

doi:10.52037/eads.2026.0004

Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics

Abstract

Objective: To assess how occlusion type, bruxism, and material characteristics influence prosthetic complications using a simulated dataset and machine learning models. Methods: A retrospective computational analysis was performed using a synthetic dataset of 1,000 simulated patients. Variables were based on clinical prevalence and prosthodontic literature, incorporating demographic, behavioral, and prosthetic parameters. Complication outcomes were generated using a literature-informed logistic risk model. Five supervised machine learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), and AdaBoost—were trained to predict prosthetic complications. Performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC AUC). Results: Key factors associated with increased complication risk included sleep bruxism, implant-opposing dentition, use of PMMA material, and a greater number of prosthetic units. Logistic Regression yielded the highest overall predictive performance (ROC AUC = 0.625), though performance across models remained modest. The SVM model failed to detect complications, likely due to the imbalanced nature of the dataset. The limited predictive power of all models underscores the multifactorial etiology and complexity of prosthetic failure. Conclusion: This study demonstrates that machine learning, when applied to a realistically simulated dataset, can identify clinically plausible risk patterns for prosthetic complications. While predictive accuracy remains moderate, this approach offers a foundation for developing future clinical decision support tools. Incorporating synthetic modeling and machine learning may enhance risk stratification and personalized treatment planning in prosthodontics. Keywords: Bruxism; Occlusion; Prosthetic Complications; Machine Learning; Simulated Data; Prosthodontics; Risk Modeling

Keywords

Thanks

The author gratefully acknowledges the guidance and support of Asst. Prof. Cengiz Evli, whose valuable insights and feedback contributed significantly to the development of this study.

References

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Details

Primary Language

English

Subjects

Dental Materials and Equipment, Prosthodontics

Journal Section

Research Article

Authors

Uğur Toprak ^*
0000-0002-2949-9189
Türkiye

Publication Date

March 25, 2026

Submission Date

July 15, 2025

Acceptance Date

December 13, 2025

Published in Issue

Year 2026 Volume: 53 Number: 1

DOI

https://doi.org/10.52037/eads.2026.0004

IZ

https://izlik.org/JA53RF58JM

Cite

RIS / Bibtex

APA

Toprak, U. (2026). Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics. European Annals of Dental Sciences, 53(1), 18-22. https://doi.org/10.52037/eads.2026.0004

AMA

1.Toprak U. Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics. EADS. 2026;53(1):18-22. doi:10.52037/eads.2026.0004

Chicago

Toprak, Uğur. 2026. “Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics”. European Annals of Dental Sciences 53 (1): 18-22. https://doi.org/10.52037/eads.2026.0004.

EndNote

Toprak U (March 1, 2026) Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics. European Annals of Dental Sciences 53 1 18–22.

IEEE

[1]U. Toprak, “Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics”, EADS, vol. 53, no. 1, pp. 18–22, Mar. 2026, doi: 10.52037/eads.2026.0004.

ISNAD

Toprak, Uğur. “Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics”. European Annals of Dental Sciences 53/1 (March 1, 2026): 18-22. https://doi.org/10.52037/eads.2026.0004.

JAMA

1.Toprak U. Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics. EADS. 2026;53:18–22.

MLA

Toprak, Uğur. “Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics”. European Annals of Dental Sciences, vol. 53, no. 1, Mar. 2026, pp. 18-22, doi:10.52037/eads.2026.0004.

Vancouver

1.Uğur Toprak. Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics. EADS. 2026 Mar. 1;53(1):18-22. doi:10.52037/eads.2026.0004