Comparing logistic regression and machine learning for obesity risk prediction: A systematic review and meta-analysis

Background: Logistic regression (LR) has traditionally been the standard method used for predicting binary health outcomes; however, machine learning (ML) methods are increasingly popular. Objective: This study aimed to compare the performance of ML and LR for obesity risk prediction, identify how LR and ML were being compared, and identify the commonly used ML methods. Methods: We conducted comprehensive searches in PubMed, Scopus, Embase, IEEE Xplore, and Web of Science databases on 24th November 2023, with no restrictions on publication dates. Meta-analyses were performed to quantify the overall predictive performance of the methods using the area under the curve (AUC) for LR, AUC for the best performing ML, as well as the difference in the AUC between the two approaches as the effect measures. Results: We included 28 studies out of 913 abstracts screened. Accuracy and sensitivity were the most commonly used performance measures. More than half of the studies used AUC, with no calibration assessment conducted in any of the studies. Decision trees followed by boosting algorithms were the most commonly used ML methods. Seventy-five percent of the studies were at high risk of bias. There were 14 included studies in the meta-analysis. The pooled AUC for LR was 0.75 (95% CI 0.70 to 0.80) and the pooled AUC for ML was 0.76 (95% CI 0.70 to 0.82). The pooled difference in logit(AUC) between ML and LR was 0.13 (95% CI -0.11 to 0.37). Conclusion: We conclude that there is no significant difference in the performance of ML and LR for obesity risk prediction. However, there is a need for improved quality of reporting of studies, the use of more performance measures particularly calibration, and to validate models in different populations.