Automated lung auscultation identification for mobile health systems using machine learning

• Main Authors: Serato, Jo Hanna Lindsey, Reyes, Rosula SJ
• Format: text
• Published: Archīum Ateneo 2018
• Subjects: Lung; Principal component analysis; Classification algorithms; Feature extraction; Microsoft Windows; Support vector machines; Prediction algorithms; Biomedical; Electrical and Computer Engineering
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/67; https://ieeexplore.ieee.org/document/8394589
• Institution: Ateneo De Manila University

An efficient classification system that aids in the computerized auscultation process was developed. A database of digital lung sounds was created from recorded lung sounds from anonymous patients using mobile application and digital stethoscopes. Efficiency of different classification algorithms to the dataset was tested, and their processing time was reduced up to 80.15% when applied with Principal Component Analysis (PCA). Among the six classification algorithms used, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) are more reliable to use in this dataset with a precision of 100% and 99.00%, respectively.