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We have developed a multi-view multi-task network (MuViTaNet) that leverages clinical data to profile multiple complications for patients. The experimental results show that MuViTaNet outperforms existing methods for profiling the development of cardiac complications (such as atrial fibrillation, heart failure, and stroke) in breast cancer survivors. Furthermore, MuViTaNet also provides an effective mechanism for interpreting its predictions in multiple perspectives, thereby helping clinicians discover the underlying mechanism triggering the onset and for making better clinical treatments in real-world scenarios.
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This content will become publicly available on January 16, 2026
Predicting Cardiac Complications of Myocardial Infarction Patients Using Machine Learning
In the United States, heart disease is the leading cause of death, killing about 695,000 people each year. Myocardial infarction (MI) is a cardiac complication which occurs when blood flow to a portion of the heart decreases or halts, leading to damage in the heart muscle. Heart failure and Atrial fibrillation (AF) are closely associated with MI. Heart failure is a common complication of MI and a risk factor for AF. Machine learning (ML) and deep learning techniques have shown potential in predicting cardiovascular conditions. However, developing a sim- plified predictive model, along with a thorough feature analysis, is challenging due to various factors, including lifestyle, age, family history, medical conditions, and clinical variables for cardiac complications prediction. This paper aims to develop simplified models with comprehensive feature analysis and data preprocessing for predicting cardiac complications, such as heart failure and atrial fibrillation linked with MI, using a publicly available dataset of myocardial infarction patients. This will help the students and health care professionals understand various factors responsible for cardiac complications through a simplified workflow. By prioritizing interpretability, this paper illustrates how simpler models, like decision trees and logistic regression, can provide transparent decision-making processes while still maintaining a balance with accuracy. Additionally, this paper examines how age-specific factors affect heart failure and atrial fibrillation conditions. Overall this research focuses on making machine learning accessible and interpretable. Its goal is to equip students and non-experts with practical tools to understand how ML can be applied in healthcare, particularly for the cardiac complications prediction for patients having MI.
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- Award ID(s):
- 2334391
- PAR ID:
- 10570736
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6248-0
- Page Range / eLocation ID:
- 7454 to 7462
- Subject(s) / Keyword(s):
- machine learning heart myocardial infarction health
- Format(s):
- Medium: X
- Location:
- Washington, DC, USA
- Sponsoring Org:
- National Science Foundation
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