Predictive modeling for peri-implantitis by using machine learning techniques

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 1...

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Autores principales:	Tomoaki Mameno, Masahiro Wada, Kazunori Nozaki, Toshihito Takahashi, Yoshitaka Tsujioka, Suzuna Akema, Daisuke Hasegawa, Kazunori Ikebe
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d9b9b3695a65420184b374821fe3a48f
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spelling	oai:doaj.org-article:d9b9b3695a65420184b374821fe3a48f2021-12-02T16:53:01ZPredictive modeling for peri-implantitis by using machine learning techniques10.1038/s41598-021-90642-42045-2322https://doaj.org/article/d9b9b3695a65420184b374821fe3a48f2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90642-4https://doaj.org/toc/2045-2322Abstract 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.Tomoaki MamenoMasahiro WadaKazunori NozakiToshihito TakahashiYoshitaka TsujiokaSuzuna AkemaDaisuke HasegawaKazunori IkebeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tomoaki Mameno Masahiro Wada Kazunori Nozaki Toshihito Takahashi Yoshitaka Tsujioka Suzuna Akema Daisuke Hasegawa Kazunori Ikebe Predictive modeling for peri-implantitis by using machine learning techniques
description	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.
format	article
author	Tomoaki Mameno Masahiro Wada Kazunori Nozaki Toshihito Takahashi Yoshitaka Tsujioka Suzuna Akema Daisuke Hasegawa Kazunori Ikebe
author_facet	Tomoaki Mameno Masahiro Wada Kazunori Nozaki Toshihito Takahashi Yoshitaka Tsujioka Suzuna Akema Daisuke Hasegawa Kazunori Ikebe
author_sort	Tomoaki Mameno
title	Predictive modeling for peri-implantitis by using machine learning techniques
title_short	Predictive modeling for peri-implantitis by using machine learning techniques
title_full	Predictive modeling for peri-implantitis by using machine learning techniques
title_fullStr	Predictive modeling for peri-implantitis by using machine learning techniques
title_full_unstemmed	Predictive modeling for peri-implantitis by using machine learning techniques
title_sort	predictive modeling for peri-implantitis by using machine learning techniques
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d9b9b3695a65420184b374821fe3a48f
work_keys_str_mv	AT tomoakimameno predictivemodelingforperiimplantitisbyusingmachinelearningtechniques AT masahirowada predictivemodelingforperiimplantitisbyusingmachinelearningtechniques AT kazunorinozaki predictivemodelingforperiimplantitisbyusingmachinelearningtechniques AT toshihitotakahashi predictivemodelingforperiimplantitisbyusingmachinelearningtechniques AT yoshitakatsujioka predictivemodelingforperiimplantitisbyusingmachinelearningtechniques AT suzunaakema predictivemodelingforperiimplantitisbyusingmachinelearningtechniques AT daisukehasegawa predictivemodelingforperiimplantitisbyusingmachinelearningtechniques AT kazunoriikebe predictivemodelingforperiimplantitisbyusingmachinelearningtechniques
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Predictive modeling for peri-implantitis by using machine learning techniques

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