What type of study is this?

This is a Systematic Review study.

December 4, 2025Health Science ReportsOpen Access

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

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Authors

FBFatemeh BarzanouniMashhad University of Medical Sciences FBFatemeh Behrouzi

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Implication

Systematic review evaluates performance of machine learning models for pneumonia diagnosis and prediction, highlighting implications for clinical decision-making.

Key Points

Machine learning approaches enhanced pneumonia diagnosis, reaching an accuracy peak of 99.63%.
DenseNet achieved 94% accuracy, while Random Forest and XGBoost garnered AUCs of 0.96 and 0.97, respectively.
Systematic review across multiple databases assessed performance metrics including accuracy and area under the curve.
Study identifies challenges of data set biases and the need for standardized reporting in clinical applications.

Cite This Study

Barzanouni et al. (2025) studied this question.

synapsesocial.com/papers/6930dc5fea1aef094cca1e2a https://doi.org/10.1002/hsr2.71446

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Implication

Systematic review evaluates performance of machine learning models for pneumonia diagnosis and prediction, highlighting implications for clinical decision-making.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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