Enhanced Prediction of Cardiovascular Disease Through Integrated Machine Learning Models Combining Clinical and...

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Heart failure (HF) is a leading cause of mortality and morbidity worldwide.
• Timely diagnosis and prognostication are crucial for improving patient outcomes and reducing healthcare costs.
• Current predictive models require enhancement for individualized patient care.

Purpose of the Study:

• To develop a novel predictive model for heart failure.
• To combine clinical criteria with demographic factors for maximized predictive performance.
• To create a reliable tool for individualized healthcare interventions in heart failure management.

Main Methods:

• Application of complex machine learning techniques (decision trees, random forest, deep learning).
• Analysis of a large, diverse dataset including echocardiographic data, biomarkers, ECG features, and demographics.
• Data preprocessing including feature normalization and imputation of missing values.

Main Results:

• Integrating clinical indicators and demographic characteristics significantly enhances predictive power.
• The hybrid model shows superior accuracy in predicting short- and long-term heart failure outcomes.
• Improved risk stratification and prognosis prediction capabilities were demonstrated.

Conclusions:

• Artificial intelligence and machine learning hold significant potential to revolutionize heart failure care.
• The proposed model offers a data-driven decision support tool for healthcare professionals.
• This research provides insights for future personalized medicine approaches in cardiology.