Computerized Medical Disease Identification Using Respiratory Sound Based on MFCC and Neural Network

For the medical domain, computer assumes a significant job in computerization and determination of the disorder. The stethoscope is an eminent and widely available traditional diagnostic instrument for the medical professionals. The computer system is used in medical science for collection and analysis of large amounts of massive data and concern accurate decision making. The respiratory sound database has been available from research community. However, full utilization of available recording device or database, there is a need to design and development of the respiratory disease identification. This paper explained the respiratory data creation and application of this data over the respiratory disorder identification. The database is collects with the help of local government hospital. The data is recorded with directional stethoscope with 3.5 jack based microphone connected with laptop or computer. The database includes 1000 recording of 7.5 h. The data is collected from 50 patients. The Mel Frequency Cepstral Coefficient technique is applied over the database for feature extraction. The pitch, energy and time are the dominant features for the disorder identification. The neural network has been used for the classification of the disorder identification. The experiment has been achieved accuracy of 91% over the two class classification. The precision of the experiment is 88% whereas sensitivity is 87%. The 9% error rate has been shows the experimental system. From the experimental analysis the author recommended the MFCC and neural network are the strong and dynamic approach in respiratory dieses determination.