Performance Evaluation of an Intelligent Lung Sound Classifier Based on an Enhanced MFCC Model

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Conference Proceeding

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The rate at which technology grew in the past years is unbelievably fast and astounding. However, chronic illnesses like respiratory diseases remains a common and widely experienced problem globally. The emergence of infectious respiratory health issues such as the coronavirus (COVID-19) had only made this enigma more harmful, causing an increase in the number of death due to respiratory illnesses. Hence, the development of modern and accurate methods to improve medical diagnosis is one of the simple step’s humans can perform to overcome such problems. In this study, the researchers proposed an enhanced model for lung sound classification using Mel Frequency Cepstral Coefficient (MFCC). The design will classify four different lung sounds, with data input taken and classified one at a time. The goal of which is to augment human intelligence and not to replace the existing lung sound classification methods. The pre-recorded lung sounds were characterized, and the researcher proposed four enhanced MFCC models with three varying designs. The data collected from feature extraction and data mining were evaluated by the machine learning algorithms Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Measures like sensitivity, specificity, and accuracy were tested to determine which model was superior. Results showed that in terms of performance metrics, KNN performed better than SVM in classifying lung sounds. Tested in three designs where the pre-emphasis was removed, and the original 44.1kHz data resampled. Model 3 using KNN sampled at a frequency of 12000Hz has reached an average accuracy of 96.92% and a blind-data accuracy of 93.33%. A specificity of 97.94% and a sensitivity of 93.83%, achieving a performance that is comparable with existing studies on lung sound classification.