Systematic Comparison of Machine Learning Algorithms to Develop and Validate Predictive Models for Periodontitis

alexandrossfakianakis shared this article with you from Inoreader Systematic Comparison of Machine Learning Algorithms to Develop and Validate Predictive Models for Periodontitis via Journal of Clinical Periodontology Abstract Aim The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis. Material 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 preprocessed, 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, [vi] negative predictive value) and two methods of data preprocessing ([i] machine learning-based feature selection, [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. This article is protected by copyright. All rights reserved. View on Web

Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.