Earticle

초록

영어

Acute myocardial infarction (AMI) is one of the leading causes of cardiovascular disease-related mortality worldwide, causing ischemic damage to the heart muscle. AMI poses a risk of sudden cardiac arrest and is associated with a high rate of recurrence, which can significantly impact daily life. Therefore, this study aims to develop a predictive model for mortality in AMI patients using machine learning. The data used in the study are from KNHDIS (2013-2022) and include demographic characteristics and disease information of discharged patients. The model was constructed using RFECV and Random Forest. Key variables influencing mortality include age, number of surgeries, and length of hospital stay. The model demonstrated high performance with an accuracy of 94% and an AUC value of 0.93.

목차

Abstract
I. INTRODUCTION
II. METHODS
A. Data Collection
B. Study Population
C. Varuable Definitions
D. Analysis Method
III. RESULTS
E. Feature Selection
F. Model Learning
IV. DISCUSSIONS& CONCLUSSION
REFERENCES

저자

• Yonghwan Moon [ Department of Health Administration Kongju national university ]
• Hyekyung Woo [ Department of Health Administration Kongju national university Institute of Health and Environment Kongju national university ] Corresponding Author

참고문헌