Predictive modeling for peri-implantitis by using machine learning techniques.
Sci Rep
; 11(1): 11090, 2021 05 27.
Article
in En
| MEDLINE
| ID: mdl-34045590
ABSTRACT
The purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC 0.71, accuracy 0.70, precision 0.72, recall 0.66, and f1-score 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Stomatitis
/
Dental Implants
/
Peri-Implantitis
/
Machine Learning
Type of study:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
Sci Rep
Year:
2021
Document type:
Article
Affiliation country: