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A machine learning-based 1-year mortality prediction model after hospital discharge for clinical patients with acute
Syed Waseem Abbas Sherazi1, Yu Jun Jeong1, Moon Hyun Jae1
1Chungbuk National University, South Korea.
Insights
Machine learning models accurately predict 1-year mortality in acute coronary syndrome patients post-discharge. These models showed improved performance over the GRACE score, aiding early detection of major adverse cardiovascular events.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Cardiovascular disease is a leading global cause of mortality.
- Early prediction and diagnosis are crucial for managing acute coronary syndrome (ACS).
Purpose of the Study:
- To develop and evaluate machine learning (ML) models for predicting 1-year mortality in ACS patients after discharge.
- To compare the performance of ML models against the GRACE score.
Main Methods:
- Utilized the Korea Acute Myocardial Infarction Registry dataset (10,813 subjects).
- Trained and evaluated four ML models using grid search and fourfold stratified cross-validation.
- Compared ML models with the GRACE score using AUC, precision, recall, accuracy, and F-score.
Main Results:
- The best ML model demonstrated improved performance compared to the GRACE score, with an average AUC improvement of 0.08.
- Identified different major prognostic factors between ML models and the GRACE score.
- The developed ML models achieved high performance in predicting mortality.
Conclusions:
- ML-based models offer a valuable tool for predicting 1-year mortality in ACS patients.
- These models can enhance early detection of major adverse cardiovascular events.
- ML models provide an alternative or complementary approach to existing risk scores like GRACE.
Abstract:
Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning-based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005-30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning-based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning-based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients.
