Automated lung auscultation identification for mobile health systems using machine learning

Automated lung auscultation identification for mobile health systems using machine learning

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...

Full description

Saved in:
Bibliographic Details
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Ateneo De Manila University
Description
Summary: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.

Similar Items