Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study - IRIT - Université Toulouse 1 Capitole Accéder directement au contenu
Article Dans Une Revue Journal of Personalized Medicine Année : 2022

Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study

Résumé

Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies.
Fichier principal
Vignette du fichier
jpm-12-00217.pdf (3.39 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03687558 , version 1 (03-06-2022)

Licence

Paternité

Identifiants

Citer

Paul Monsarrat, David Bernard, Mathieu Marty, Chiara Cecchin-Albertoni, Emmanuel Doumard, et al.. Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study. Journal of Personalized Medicine, 2022, 12 (2), pp.217. ⟨10.3390/jpm12020217⟩. ⟨hal-03687558⟩
158 Consultations
62 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More