December 5, 2025Methods and ProtocolsOpen Access

A Study Protocol on Risk Prediction Modelling of Mortality and In-Hospital Major Bleeding Following Percutaneous Coronary Intervention in an Australian Population: Machine Learning Approach

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Protocol develops machine learning models for mortality and bleeding risk in PCI patients, indicating improvements in clinical decision-making.

Mortality was predicted using machine learning models that incorporate variables from 104,665 PCI cases.
Key predictive factors were identified using the Boruta method for effective risk assessment and patient outcomes.
Analysis employed methods like 10-fold cross-validation and Adaptive Synthetic resampling to enhance model accuracy.
Findings aim to improve clinical decision-making in Percutaneous Coronary Intervention through robust outcome predictions.

Chowdhury et al. (2025) studied this question.

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December 5, 2025Methods and ProtocolsOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Protocol develops machine learning models for mortality and bleeding risk in PCI patients, indicating improvements in clinical decision-making.

Mortality was predicted using machine learning models that incorporate variables from 104,665 PCI cases.
Key predictive factors were identified using the Boruta method for effective risk assessment and patient outcomes.
Analysis employed methods like 10-fold cross-validation and Adaptive Synthetic resampling to enhance model accuracy.
Findings aim to improve clinical decision-making in Percutaneous Coronary Intervention through robust outcome predictions.

Chowdhury et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark