PMID- 24970564
OWN - NLM
STAT- MEDLINE
DCOM- 20141012
LR  - 20211021
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 15
DP  - 2014 Jun 27
TI  - A comparative study of the SVM and K-nn machine learning algorithms for the 
      diagnosis of respiratory pathologies using pulmonary acoustic signals.
PG  - 223
LID - 10.1186/1471-2105-15-223 [doi]
AB  - BACKGROUND: Pulmonary acoustic parameters extracted from recorded respiratory 
      sounds provide valuable information for the detection of respiratory pathologies. 
      The automated analysis of pulmonary acoustic signals can serve as a differential 
      diagnosis tool for medical professionals, a learning tool for medical students, 
      and a self-management tool for patients. In this context, we intend to evaluate 
      and compare the performance of the support vector machine (SVM) and K-nearest 
      neighbour (K-nn) classifiers in diagnosis respiratory pathologies using 
      respiratory sounds from R.A.L.E database. RESULTS: The pulmonary acoustic signals 
      used in this study were obtained from the R.A.L.E lung sound database. The 
      pulmonary acoustic signals were manually categorised into three different groups, 
      namely normal, airway obstruction pathology, and parenchymal pathology. The 
      mel-frequency cepstral coefficient (MFCC) features were extracted from the 
      pre-processed pulmonary acoustic signals. The MFCC features were analysed by 
      one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The 
      performances of the classifiers were analysed using the confusion matrix 
      technique. The statistical analysis of the MFCC features using one-way ANOVA 
      showed that the extracted MFCC features are significantly different (p < 0.001). 
      The classification accuracies of the SVM and K-nn classifiers were found to be 
      92.19% and 98.26%, respectively. CONCLUSION: Although the data used to train and 
      test the classifiers are limited, the classification accuracies found are 
      satisfactory. The K-nn classifier was better than the SVM classifier for the 
      discrimination of pulmonary acoustic signals from pathological and normal 
      subjects obtained from the RALE database.
FAU - Palaniappan, Rajkumar
AU  - Palaniappan R
AD  - AI-Rehab Research Group, Kampus Pauh Putra, Universiti Malaysia Perlis (UniMAP), 
      Perlis, Malaysia. prkmect@gmail.com.
FAU - Sundaraj, Kenneth
AU  - Sundaraj K
FAU - Sundaraj, Sebastian
AU  - Sundaraj S
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20140627
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
SB  - IM
MH  - *Acoustics
MH  - *Algorithms
MH  - Analysis of Variance
MH  - Diagnosis, Differential
MH  - Humans
MH  - *Lung/pathology
MH  - Respiratory Tract Diseases/*diagnosis/pathology
MH  - *Signal Processing, Computer-Assisted
MH  - *Support Vector Machine
PMC - PMC4094993
EDAT- 2014/06/28 06:00
MHDA- 2014/10/13 06:00
PMCR- 2014/06/27
CRDT- 2014/06/28 06:00
PHST- 2013/12/04 00:00 [received]
PHST- 2014/06/18 00:00 [accepted]
PHST- 2014/06/28 06:00 [entrez]
PHST- 2014/06/28 06:00 [pubmed]
PHST- 2014/10/13 06:00 [medline]
PHST- 2014/06/27 00:00 [pmc-release]
AID - 1471-2105-15-223 [pii]
AID - 10.1186/1471-2105-15-223 [doi]
PST - epublish
SO  - BMC Bioinformatics. 2014 Jun 27;15:223. doi: 10.1186/1471-2105-15-223.