ArticleScientific reports2024
Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Symptom-based diagnostic models for common respiratory viral infections: a machine learning and natural language processing study.Infectious Disease Modelling · 2026Article
- Mycoplasma pneumoniae infections in the Post-COVID era: recent advances from pathogenesis to clinical management.Infection · 2026Review
- A comparative evaluation of sufficient dimension reduction and traditional statistical methods for composite biomarker score construction in diagnostic classification.BMC medical research methodology · 2025Article
- Field-oriented assessment of bovine tuberculosis in Tunisian cattle: IDR, PCRWorld journal of microbiology & biotechnology · 2025Article
- Predicting the progressive-free survival of ALK-positive lung cancer: machine learning algorithm based on habitat imaging.Journal of thoracic disease · 2025Article
- A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia.Scientific reports · 2025Article
- Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning.BMC neurology · 2025Article
- Salp swarm-optimized machine learning models for predicting preoperative aortic rupture risk in acute type a aortic dissection patients.Frontiers in physiology · 2025Article
- Machine learning-guided feature selection and predictive model construction for attention-deficit/hyperactivity disorder.Frontiers in psychiatry · 2025Article
- Biomarkers associated with the diagnosis and prognosis ofFrontiers in cellular and infection microbiology · 2025Review
- Discovery of a preliminary urinary metabolite panel for Parkinson's disease: a pilot study using paired patient-spouse samples and machine learning consensus.Frontiers in neurologyArticle
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Authors and funding
9 authors.
Funding
Abstract
The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning techniques, such as random forest analysis, can significantly improve diagnostic accuracy. We conducted a retrospective analysis of clinical and laboratory data from 214 patients with acute respiratory infections, collected between October 2022 and October 2023 at the Second Hospital of Nanping. The study population was categorized into three groups: COVID-19 positive (n = 52), MP positive (n = 140), and co-infected (n = 22). Key biomarkers, including C-reactive protein (CRP), procalcitonin (PCT), interleukin- 6 (IL-6), and white blood cell (WBC) counts, were evaluated. Correlation analyses were conducted to assess relationships between biomarkers within each group. The random forest analysis was applied to evaluate the discriminative power of these biomarkers. The random forest model demonstrated high classification performance, with area under the ROC curve (AUC) scores of 0.86 (95% CI: 0.70-0.97) for COVID-19, 0.79 (95% CI: 0.64-0.92) for MP, 0.69 (95% CI: 0.50-0.87) for co-infections, and 0.90 (95% CI: 0.83-0.95) for the micro-average ROC. Additionally, the precision-recall curve for the random forest classifier showed a micro-average AUC of 0.80 (95% CI: 0.69-0.91). Confusion matrices highlighted the model's accuracy (0.77) and biomarker relationships. The SHAP feature importance analysis indicated that age (0.27), CRP (0.25), IL6 (0.14), and PCT (0.14) were the most significant predictors. The integration of computational methods, particularly random forest analysis, in evaluating clinical and biomarker data presents a promising approach for enhancing diagnostic processes for infectious diseases. Our findings support the use of specific biomarkers in differentiating between COVID-19 and MP, potentially leading to more targeted and effective diagnostic strategies. This study underscores the potential of machine learning techniques in improving disease classification in the era of precision medicine.
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