Applying Machine Learning Techniques to Find Important Attributes for Heart Failure Severity Assessment
- DOI
- 10.5121/ijcsea.2017.7501
- Published
- 2017-10-30
- Container
- International Journal of Computer Science, Engineering and Applications
- Publisher
- Academy and Industry Research Collaboration Center (AIRCC)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.5121/ijcsea.2017.7501,
title = {Applying Machine Learning Techniques to Find Important Attributes for Heart Failure Severity Assessment},
author = {Puram Surya Prudvi and Ershad Sharifahmadian},
year = {2017},
journal = {International Journal of Computer Science, Engineering and Applications},
doi = {10.5121/ijcsea.2017.7501},
url = {https://doi.org/10.5121/ijcsea.2017.7501}
}RIS
TY - JOUR TI - Applying Machine Learning Techniques to Find Important Attributes for Heart Failure Severity Assessment AU - Puram Surya Prudvi AU - Ershad Sharifahmadian PY - 2017 JO - International Journal of Computer Science, Engineering and Applications DO - 10.5121/ijcsea.2017.7501 UR - https://doi.org/10.5121/ijcsea.2017.7501 ER -
APA
Prudvi, P. S., & Sharifahmadian, E. (2017). Applying Machine Learning Techniques to Find Important Attributes for Heart Failure Severity Assessment. International Journal of Computer Science, Engineering and Applications. https://doi.org/10.5121/ijcsea.2017.7501
Source records
- crossref · retrieved 2026-09-26T05:59:50.900Z