Development of Predictive Models in Heart Failure.

Area of Science:

• Cardiology
• Medical Informatics
• Machine Learning

Background:

• Heart failure poses a significant global health challenge.
• Early diagnosis and prevention are crucial for improving patient outcomes.
• Traditional diagnostic methods have limitations in prediction accuracy.

Purpose of the Study:

• To develop and evaluate machine learning models for heart failure prediction.
• To assess the efficacy of various machine learning algorithms in identifying high-risk patients.
• To create a practical application for healthcare professionals.

Main Methods:

• Analysis of comprehensive patient characteristics.
• Application of machine learning algorithms including random forest and gradient boosting.
• Development of a predictive model using the Django framework.

Main Results:

• Highly accurate predictions for heart failure were achieved using machine learning.
• The developed Django application demonstrated effectiveness with real-world data.
• The models identified key patient factors contributing to heart failure risk.

Conclusions:

• Machine learning models offer reliable tools for early heart failure diagnosis and prevention.
• The study highlights the potential of AI in enhancing medical prediction capabilities.
• Further research is warranted to refine models and explore broader clinical applications.