Abstract
Heart sound analysis is a preliminary procedure performed by a physician and involves examining the heart beats to detect the symptoms of cardiovascular diseases (CVDs). With recent developments in clinical science and the availability of devices to capture heart beats, researchers are now exploring the possibility of a machine assisted heart sound analysis system that can augment the clinical expertise of the physician in early detection of CVD. In this paper, we study the application of machine learning algorithms in classifying abnormal/normal heart sounds based on the short (≤120 s) audio phonocardiogram (PCG) recordings. To this end, we use the largest public audio PCG dataset released as part of the 2016 PhysioNet/Cardiology in Computing Challenge. The data comes from different patients, most of who have had no previous history of cardiac disease and some with known cardiac diseases. In our study, we use these audio recordings to train three different classification algorithms and discuss the effects of class imbalance (normal vs. abnormal) on the precision-recall trade-off of the prediction task. Specifically, our goal is to find a suitable model that takes into account the inherent imbalance and optimize the precision-recall trade-off with a higher emphasis on increasing recall. Bagged random forest models with majority (normal) class under sampling gave us the best configuration resulting in average recall over 91% with nearly 64% average precision.
| Original language | English |
|---|---|
| Title of host publication | Big Data Analytics - 5th International Conference, BDA 2017, Proceedings |
| Editors | Ashish Sureka, Sharma Chakravarthy, P. Krishna Reddy, Subhash Bhalla |
| Pages | 179-194 |
| Number of pages | 16 |
| DOIs | |
| State | Published - 2017 |
| Event | 5th International Conference on Big Data Analytics, BDA 2017 - Hyderabad, India Duration: Dec 12 2017 → Dec 15 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10721 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 5th International Conference on Big Data Analytics, BDA 2017 |
|---|---|
| Country/Territory | India |
| City | Hyderabad |
| Period | 12/12/17 → 12/15/17 |
Bibliographical note
Funding Information:Acknowledgements. We thank anonymous reviewers for their honest and constructive criticism of our paper. Our work is primarily supported by the National Library of Medicine through grant R21LM012274. We are also supported by the National Center for Advancing Translational Sciences through grant UL1TR001998. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Funding Information:
We thank anonymous reviewers for their honest and constructive criticism of our paper. Our work is primarily supported by the National Library of Medicine through grant R21LM012274. We are also supported by the National Center for Advancing Translational Sciences through grant UL1TR001998. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Publisher Copyright:
© Springer International Publishing AG 2017.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science (all)
