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Does machine learning have a high performance to predict obesity among adults and older adults? A systematic review and meta-analysis. | LitMetric

AI Article Synopsis

  • - This study investigates how effective machine learning models are at predicting obesity by reviewing existing literature and performing a meta-analysis including 14 studies that reported results using the Area Under the ROC Curve (AUC).
  • - The analysis showed that the random forest algorithm was the most accurate, with an AUC of 0.86, followed closely by logistic regression at 0.85, while gradient boosting had the lowest effectiveness at 0.77.
  • - The study concludes that while machine learning models have shown promising results for obesity prediction, there is a need for improved data comparability, larger datasets, and a wider variety of machine learning models in future research.

Article Abstract

Aim: Machine learning may be a tool with the potential for obesity prediction. This study aims to review the literature on the performance of machine learning models in predicting obesity and to quantify the pooled results through a meta-analysis.

Data Synthesis: A systematic review and meta-analysis were conducted, including studies that used machine learning to predict obesity. Searches were conducted in October 2023 across databases including LILACS, Web of Science, Scopus, Embase, and CINAHL. We included studies that utilized classification models and reported results in the Area Under the ROC Curve (AUC) (PROSPERO registration: CRD42022306940), without imposing restrictions on the year of publication. The risk of bias was assessed using an adapted version of the Transparent Reporting of a multivariable prediction model for individual Prognosis or Diagnosis (TRIPOD). Meta-analysis was conducted using MedCalc software. A total of 14 studies were included, with the majority demonstrating satisfactory performance for obesity prediction, with AUCs exceeding 0.70. The random forest algorithm emerged as the top performer in obesity prediction, achieving an AUC of 0.86 (95%CI: 0.76-0.96; I: 99.8%), closely followed by logistic regression with an AUC of 0.85 (95%CI: 0.75-0.95; I: 99.6%). The least effective model was gradient boosting, with an AUC of 0.77 (95%CI: 0.71-0.82; I: 98.1%).

Conclusion: Machine learning models demonstrated satisfactory predictive performance for obesity. However, future research should utilize more comparable data, larger databases, and a broader range of machine learning models.

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Source
http://dx.doi.org/10.1016/j.numecd.2024.05.020DOI Listing

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