Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis

Aim The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis. Materials and Methods Using national survey data from Taiwan (n = 3453) and the United States (n = 3685), predictors of periodontitis were extra...

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Bibliographic Details
Published in:Journal of clinical periodontology 2022-10, Vol.49 (10), p.958-969
Main Authors: Bashir, Nasir Z., Rahman, Zahid, Chen, Sam Li‐Sheng
Format: Article
Language:English
Subjects:
Algorithms
computing
Diagnosis, Epidemiology and Associated Co‐morbidities
Gum disease
Humans
Learning algorithms
Machine Learning
Original
Periodontitis
Periodontitis - diagnosis
Prediction models
predictive modelling
Predictive Value of Tests
ROC Curve
statistics
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Summary:Aim The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis. Materials and Methods Using national survey data from Taiwan (n = 3453) and the United States (n = 3685), predictors of periodontitis were extracted from the datasets and pre‐processed, and then 10 machine learning algorithms were trained to develop predictive models. The models were validated both internally (bootstrap sampling) and externally (alternative country's dataset). The algorithms were compared across six performance metrics ([i] area under the curve for the receiver operating characteristic [AUC], [ii] accuracy, [iii] sensitivity, [iv] specificity, [v] positive predictive value, and [vi] negative predictive value) and two methods of data pre‐processing ([i] machine‐learning‐based feature selection and [ii] dimensionality reduction into principal components). Results Many algorithms showed extremely strong performance during internal validation (AUC > 0.95, accuracy > 95%). However, this was not replicated in external validation, where predictive performance of all algorithms dropped off drastically. Furthermore, predictive performance differed according to data pre‐processing methodology and the cohort on which they were trained. Conclusions Larger sample sizes and more complex predictors of periodontitis are required before machine learning can be leveraged to its full potential.
ISSN:0303-6979
1600-051X
DOI:10.1111/jcpe.13692