Evaluation of Machine Learning Methods Developed for Prediction and Diagnosis of Pneumonia: A Systematic Review

Abstract

Background and Aims: With the increasing prevalence of pneumonia, machine learning (ML) models have been increasingly utilized to diagnose, predict, and treat pneumonia due to their ability to manage complex datasets. This systematic review evaluates the performance and quality of ML models developed for pneumonia prediction, diagnosis, and treatment, following the statistical reporting guidelines of Assel et al. (2018). Methods: On 15 January 2024, a systematic review was conducted in PubMed, Scopus, Web of Science, and Google Scholar using the PRISMA checklist. Articles developing or validating ML models for pneumonia were included. Performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were extracted with confidence intervals where available. Results: Of 11,545 screened articles, 42 studies evaluating 125 ML models were included. For pneumonia diagnosis, DenseNet achieved the highest accuracy of 94% (95% CI: 92%–96%), while Random Forest and XGBoost were the most effective for prediction, with AUCs of 0.96 (95% CI: 0.94–0.98) and 0.97 (95% CI: 0.95–0.99), respectively. Neural networks (n = 15) showed a peak accuracy of 98.94% (95% CI: 97.5%–99.5%), and ResNet demonstrated superior performance with an accuracy of 99.63% (95% CI: 98.8%–99.9%). Potential biases in datasets and limited generalizability were noted. Conclusion: ML algorithms significantly improve pneumonia diagnosis and prediction, optimizing clinical decision-making. However, data set biases and generalizability challenges highlight the need for standardized reporting and robust validation.