Publication: Comparison of Machine Learning Models for Prediction of In-Hospital Adverse Outcomes after Percutaneous Coronary Intervention
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Issued Date
2024-01-01
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Scopus ID
2-s2.0-85211337035
Journal Title
9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024
Start Page
39
End Page
43
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SCOPUS
Bibliographic Citation
9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024 (2024) , 39-43
Suggested Citation
Chanmanacharoen I.R., Jampa-Ngern S., Senavongse W. Comparison of Machine Learning Models for Prediction of In-Hospital Adverse Outcomes after Percutaneous Coronary Intervention. 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024 (2024) , 39-43. 43. doi:10.1109/BCD61269.2024.10743085 Retrieved from: https://hdl.handle.net/20.500.14740/20547
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Abstract
Cardiovascular disease is a non-communicable disease that is one of the leading causes of mortality worldwide, and its prevalence is growing year after year. Most cardiovascular diseases require percutaneous coronary intervention (PCI) to remove blood vessel blockages, However, this therapy carries risks of adverse outcomes, such as in-hospital bleeding and mortality. Currently, machine learning is widely used in prediction and decision-making support for clinicians. According to the above reasons, this study demonstrates using and comparing four common machine learning algorithms to predict in-hospital bleeding and in-hospital mortality outcomes. Finally, this study found that the XGBoost model accomplished the most outstanding in-hospital bleeding and mortality results. The performance of in-hospital bleeding shows Accuracy of 0.987 (95% CI: 0.980 - 0.994). In-hospital mortality shows Accuracy of 0.972 (95% CI: 0.962 - 0.982).