Name
Title | ||
|---|---|---|
Hybrid SVM/CART classification of pathogenic species of bacterial meningitis with surface-enhanced Raman scattering |
Abstract | ||
|---|---|---|
Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens: Streptococcus pneumoniae(Spn), Streptococcus agalactiae (group B streptococcus, GBS), Staphylococcus aureus (Sa), Pseudomonas aeruginosae (Psa), Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), Neisseria meningitidis (Nm), Listeria monocy-togenes (Lm), Haemophilus influenzae (Hi), and Escherichia coli (E. coli). These samples were obtained from patients in National Taiwan University Hospital, and were believed to represent the real diversity of clinical pathogens. Using the support vector machine (SVM) method, the classification accuracy can achieve around 88%. However, we noted that SVM cannot distinguish between [E. coli, Kp] and [Sa, Hi] due to the fact that the global features of these two groups of pathogens are very similar. We therefore incorporated a classification tree method that can focus on local differences in classification rules. This improved the accuracy to 90%. To get a better understanding of the SERS signals, we also compared several other classification methods. In addition, rule extraction method which attempts to explain why classifier fail or succeed is also discussed. Our preliminary results are interesting, encouraging, and await more thorough investigation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BIBM.2010.5706600 | BIBM |
Keywords | Field | DocType |
acinetobacter baumannii,streptococcus agalactiae,klebsiella pneumoniae,pseudomonas aeruginosae,cart,trees (mathematics),haemophilus influenzae,regression analysis,microorganisms,bacterial meningitis pathogen diagnosis,medical signal processing,surface enhanced raman scattering,spectral analysis,classification tree method,sers platform,bacterial meningitis pathogenic species,svm,neisseria meningitidis,knowledge engineering,support vector machine,sers,rule extraction method,signal classification,classification and regression tree analysis,escherichia coli,staphylococcus aureus,pathogen sers spectra,hybrid svm-cart classification,patient diagnosis,support vector machines,listeria monocytogenes,streptococcus pneumoniae,hybrid svm,pathogen surface chemical components,accuracy,raman scattering,kernel,testing | Streptococcus,Haemophilus influenzae,Neisseria meningitidis,Streptococcus pneumoniae,Biology,Acinetobacter baumannii,Microbiology,Klebsiella pneumoniae,Escherichia coli,Streptococcus agalactiae | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-4244-8307-5 | 2 |
PageRank | References | Authors |
0.41 | 5 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chung-Yueh Huang | 1 | 4 | 0.79 |
| Tsung-Heng Tsai | 2 | 53 | 7.81 |
| Bing-Cheng Wen | 3 | 4 | 0.79 |
| Chia-Wen Chung | 4 | 4 | 0.79 |
| Yung-Jui Li | 5 | 4 | 0.79 |
| Ya-Ching Chuang | 6 | 4 | 0.79 |
| Wenjie Lin | 7 | 10 | 1.57 |
| Li-Li Li | 8 | 4 | 0.79 |
| Juen-Kai Wang | 9 | 5 | 1.54 |
| Yuh-Lin Wang | 10 | 5 | 1.54 |
| Chi-Hung Lin | 11 | 217 | 34.67 |
| Da-Wei Wang | 12 | 39 | 5.93 |