| Autor: |
Sumon MSI; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar., Hossain MSA; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar., Al-Sulaiti H; Department of Biomedical Sciences, College of Health Sciences, Qatar University, Doha 2713, Qatar.; Biomedical Research Center, Qatar University, Doha 2713, Qatar., Yassine HM; Biomedical Research Center, Qatar University, Doha 2713, Qatar., Chowdhury MEH; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar. |
| Jazyk: |
angličtina |
| Zdroj: |
Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2024 Oct 04; Vol. 14 (19). Date of Electronic Publication: 2024 Oct 04. |
| DOI: |
10.3390/diagnostics14192214 |
| Abstrakt: |
Background/Objectives: Nasal and nasopharyngeal swabs are commonly used for detecting respiratory viruses, including influenza, which significantly alters host cell metabolites. This study aimed to develop a machine learning model to identify biomarkers that differentiate between influenza-positive and -negative cases using clinical metabolomics data. Method: A publicly available dataset of 236 nasopharyngeal samples screened via liquid chromatography-quadrupole time-of-flight (LC/Q-TOF) mass spectrometry was used. Among these, 118 samples tested positive for influenza (40 A H1N1, 39 A H3N2, 39 Influenza B), while 118 were negative controls. A stacking-based model was proposed using the top 20 selected features. Thirteen machine learning models were initially trained, and the top three were combined using predicted probabilities to form a stacking classifier. Results : The ExtraTrees stacking model outperformed other models, achieving 97.08% accuracy. External validation on a prospective cohort of 96 symptomatic individuals (48 positive and 48 negatives for influenza) showed 100% accuracy. SHAP values were used to enhance model explainability. Metabolites such as Pyroglutamic Acid (retention time: 0.81 min, m / z : 84.0447) and its in-source fragment ion (retention time: 0.81 min, m / z : 130.0507) showed minimal impact on influenza-positive cases. On the other hand, metabolites with a retention time of 10.34 min and m / z 106.0865, and a retention time of 8.65 min and m / z 211.1376, demonstrated significant positive contributions. Conclusions : This study highlights the effectiveness of integrating metabolomics data with machine learning for accurate influenza diagnosis. The stacking-based model, combined with SHAP analysis, provided robust performance and insights into key metabolites influencing predictions. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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