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

Nasir Z. Bashir, Zahid Rahman, Sam Li Sheng Chen

研究成果: 雜誌貢獻文章同行評審

5 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
頁(從 - 到)958-969
頁數12
期刊Journal of Clinical Periodontology
49
發行號10
DOIs
出版狀態已發佈 - 10月 2022

ASJC Scopus subject areas

  • 牙周病

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