Prediction of In-hospital Mortality in Acute Myocardial Infarction Patients with Typical Chest Pain

What is it about?

This paper proposes a machine learning-based prediction model for the in-hospital mortality in Acute myocardial infarction (AMI) patients with typical chest pain. To understand the principle of the black-box prediction model, a Shapley additive explanations (SHAP) method is applied to the ML-based prediction model. And two kinds of data sampling methods are applied to handle the class imbalance problem on the experimental data.

Featured Image

Photo by Robina Weermeijer on Unsplash

Photo by Robina Weermeijer on Unsplash

Why is it important?

Acute myocardial infarction (AMI) is the leading cause of hospital admissions and death all over the world and chest pain is the most common presenting complaint of AMI. Our findings show that the logistic regression with the Adaptive Synthetic (ADASYN) approach achieved the highest performance. Moreover, the SHAP technique enhanced the transparency of the ML model and can be a good reference for doctors to support their decisions in real life.