ArticleFrontiers in public health2022
Machine learning-assisted prediction of pneumonia based on non-invasive measures.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 23 citations in OpenAlex.
- Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from lobar pneumonia: a multicenter investigation.European journal of medical research · 2025Trial
- Prediction of atelectasis inFrontiers in pediatrics · 2026Article
- Artificial Intelligence Applications in Pneumonia: Diagnosis and Outcome Prediction.Current pulmonology reports · 2026Review
- Evaluation of Machine Learning Methods Developed for Prediction and Diagnosis of Pneumonia: A Systematic Review.Health science reports · 2025Review
- A machine learning model including pentraxin-3 as predictor of outcomes in community-acquired pneumonia.Journal of translational medicine · 2025Observational
- A Novel Knowledge Fusion Ensemble for Diagnostic Differentiation of Pediatric Pneumonia and Acute Bronchitis.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a prognostic nomogram for predicting hypostatic pneumonia risk in large vessel occlusion stroke after endovascular therapy patients.Frontiers in neurology · 2025Article
- Explainable machine learning model predicts response to adjuvant therapy after radical cystectomy in bladder cancer.Frontiers in oncology · 2025Article
- Determinants of community-acquired pneumonia among under-five children in Awi Zone, Northwest Ethiopia.Frontiers in public health · 2025Article
- Efficient federated learning for pediatric pneumonia on chest X-ray classification.Scientific reports · 2024Article
- Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity.International journal of environmental research and public health · 2023Article
- Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms-Results from a Retrospective Study.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China.Frontiers in medicine · 2023Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pneumonia is an infection of the lungs that is characterized by high morbidity and mortality. The use of machine learning systems to detect respiratory diseases Methods: We perform machine-learning analysis on 535 different patients, each with 45 features. Data normalization to rescale all real-valued features was performed. Since it is a binary problem, we categorized each patient into one class at a time. We designed three experiments to evaluate the models: (1) feature selection techniques to select appropriate features for the models, (2) experiments on the imbalanced original dataset, and (3) experiments on the SMOTE data. We then compared eight machine learning models to evaluate their effectiveness in predicting pneumonia. Results: Biomarkers such as C-reactive protein and procalcitonin demonstrated the most significant discriminating power. Ensemble machine learning models such as RF (accuracy = 92.0%, precision = 91.3%, recall = 96.0%, f1-Score = 93.6%) and XGBoost (accuracy = 90.8%, precision = 92.6%, recall = 92.3%, f1-score = 92.4%) achieved the highest performance accuracy on the original dataset with AUCs of 0.96 and 0.97, respectively. On the SMOTE dataset, RF and XGBoost achieved the highest prediction results with f1-scores of 92.0 and 91.2%, respectively. Also, AUC of 0.97 was achieved for both RF and XGBoost models. Conclusions: Our models showed that in the diagnosis of pneumonia, individual clinical history, laboratory indicators, and symptoms do not have adequate discriminatory power. We can also conclude that the ensemble ML models performed better in this study.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.