Automated Lung Auscultation Identification for Mobile Health Systems Using Machine Learning

被引：0
|
作者：
Lindsey Serato, Jo Hanna [1 ]
Reyes, Rosula [1 ]
机构：
[1] Ateneo Manila Univ, Katipunan Ave, Quezon City, Metro Manila, Philippines
来源：
PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ) | 2018年
关键词：
classification algorithm; KNN; SVM; auscultation; predictive analysis;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：287 / 290
页数：4
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