Current applications of big data and machine learning in cardiology

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Machine learning (ML) offers predictive capabilities without explicit programming, crucial for analyzing large datasets.
• Cardiology shows significant interest in ML for its potential to enhance patient care and advance precision medicine.

Purpose of the Study:

• To explore the diverse applications of machine learning in cardiology.
• To highlight ML's role in improving diagnostic accuracy, risk assessment, and therapeutic strategies.

Main Methods:

• Supervised and unsupervised learning algorithms are employed to train ML models.
• ML is applied to cardiac imaging, electrocardiographic (ECG) analysis, cardiovascular risk stratification, and genomic data.

Main Results:

• ML facilitates automated scoring, prognostic phenotype differentiation, and quantification of cardiac function.
• ML demonstrates efficacy in early anomaly detection in ECGs and predicting cardiovascular events.
• Genomic assessment for cardiovascular diseases is an emerging area for ML application.

Conclusions:

• Machine learning significantly contributes to earlier disease diagnosis and the development of patient-tailored therapies.
• ML enables the identification of predictive characteristics for various cardiovascular conditions, paving the way for precision cardiology.