Procedure to Improve the Accuracy of Dental Implant Failures by Data Science Techniques

Nowadays, the prediction about dental implant failure is determined through clinical and radiological evaluation. For this reason, predictions are highly dependent on the Implantologists’ experience. In addition, it is extremely crucial to detect in time if a dental implant is going to fail, due to...

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Autores principales: Ganz, Nancy, Ares, Alicia E., Kuna, Horacio Daniel
Formato: Articulo
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Informáticas
Feature selection
Classifier
Ensemble
Failure
Dental implants
Selección de características
Clasificación
Integración
Implantes Dentales
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/128264
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-128264
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Feature selection
Classifier
Ensemble
Failure
Dental implants
Selección de características
Clasificación
Integración
Implantes Dentales
spellingShingle Ciencias Informáticas
Feature selection
Classifier
Ensemble
Failure
Dental implants
Selección de características
Clasificación
Integración
Implantes Dentales
Ganz, Nancy
Ares, Alicia E.
Kuna, Horacio Daniel
Procedure to Improve the Accuracy of Dental Implant Failures by Data Science Techniques
topic_facet Ciencias Informáticas
Feature selection
Classifier
Ensemble
Failure
Dental implants
Selección de características
Clasificación
Integración
Implantes Dentales
description Nowadays, the prediction about dental implant failure is determined through clinical and radiological evaluation. For this reason, predictions are highly dependent on the Implantologists’ experience. In addition, it is extremely crucial to detect in time if a dental implant is going to fail, due to time, cost, trauma to the patient, postoperative problems, among others. This paper proposes a procedure using multiple feature selection methods and classification algorithms to improve the accuracy of dental implant failures in the province of Misiones, Argentina, validated by human experts. The experimentation is performed with two data sets, a set of dental implants made for the case study and an artificially generated set. The proposed approach allows to know the most relevant features and improve the accuracy in the classification of the target class (dental implant failure), to avoid biasing the decision making based on the application and results of individual methods. The proposed approach achieves an accuracy of 79% of failures, while individual classifiers achieve a maximum of 72%.
format Articulo
Articulo
author Ganz, Nancy
Ares, Alicia E.
Kuna, Horacio Daniel
author_facet Ganz, Nancy
Ares, Alicia E.
Kuna, Horacio Daniel
author_sort Ganz, Nancy
title Procedure to Improve the Accuracy of Dental Implant Failures by Data Science Techniques
title_short Procedure to Improve the Accuracy of Dental Implant Failures by Data Science Techniques
title_full Procedure to Improve the Accuracy of Dental Implant Failures by Data Science Techniques
title_fullStr Procedure to Improve the Accuracy of Dental Implant Failures by Data Science Techniques
title_full_unstemmed Procedure to Improve the Accuracy of Dental Implant Failures by Data Science Techniques
title_sort procedure to improve the accuracy of dental implant failures by data science techniques
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/128264
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