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Development and international validation of logistic regression and machine‐learning models for the prediction of 10‐year molar loss
Author(s) -
Troiano Giuseppe,
Nibali Luigi,
Petsos Hari,
Eickholz Peter,
Saleh Muhammad H. A.,
Santamaria Pasquale,
Jian Jao,
Shi Shuwen,
Meng Huanxin,
Zhurakivska Khrystyna,
Wang HomLay,
Ravidà Andrea
Publication year - 2023
Publication title -
journal of clinical periodontology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.456
H-Index - 151
eISSN - 1600-051X
pISSN - 0303-6979
DOI - 10.1111/jcpe.13739
Subject(s) - logistic regression , artificial intelligence , naive bayes classifier , machine learning , artificial neural network , decision tree , molar , support vector machine , random forest , predictive modelling , receiver operating characteristic , cross validation , regression , computer science , multinomial logistic regression , statistics , dentistry , mathematics , medicine
Abstract Aim To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. Materials and Methods Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine‐learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K‐nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis‐related molar loss. In addition, the ensembled model showed the best calibration performance. Conclusions Through a multi‐centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice.