Suitability of machine learning models for prediction of clinically defined Stage III/IV periodontitis from questionnaires and demographic data in Danish cohorts.
diagnostics
machine learning
periodontitis
predictive modelling
Journal
Journal of clinical periodontology
ISSN: 1600-051X
Titre abrégé: J Clin Periodontol
Pays: United States
ID NLM: 0425123
Informations de publication
Date de publication:
10 Sep 2023
10 Sep 2023
Historique:
revised:
14
07
2023
received:
14
12
2022
accepted:
17
08
2023
medline:
11
9
2023
pubmed:
11
9
2023
entrez:
11
9
2023
Statut:
aheadofprint
Résumé
To evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self-report questionnaires and demographic data. Self-reported measures of periodontitis, demographic data and clinically established Stage III/IV periodontitis status were extracted from two Danish population-based cohorts (The Copenhagen Aging and Midlife Biobank [CAMB] and The Danish Health Examination Survey [DANHES]) and used to develop cross-validated machine learning models for the prediction of clinically established Stage III/IV periodontitis. Models were trained using 10-fold cross-validations repeated three times on the CAMB dataset (n = 1476), and the resulting models were validated in the DANHES dataset (n = 3585). The prevalence of Stage III/IV periodontitis was 23.2% (n = 342) in the CAMB dataset and 9.3% (n = 335) in the DANHES dataset. For the prediction of clinically established Stage III/IV periodontitis in the CAMB cohort, models reached area under the receiver operating characteristics (AUROCs) of 0.67-0.69, sensitivities of 0.58-0.64 and specificities of 0.71-0.80. In the DANHES cohort, models derived from the CAMB cohort achieved AUROCs of 0.64-0.70, sensitivities of 0.44-0.63 and specificities of 0.75-0.84. Applying cross-validated machine learning algorithms to demographic data and self-reported measures of periodontitis resulted in models with modest capabilities for the prediction of Stage III/IV periodontitis in two Danish cohorts.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Velux Foundation
ID : VELUX26145
Organisme : Velux Foundation
ID : 31539
Informations de copyright
© 2023 The Authors. Journal of Clinical Periodontology published by John Wiley & Sons Ltd.
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