Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review.

Area of Science:

• Cardiology
• Medical Informatics
• Data Science

Background:

• Heart failure is a major global cause of mortality.
• High treatment costs and readmission rates strain healthcare systems.
• Early prediction can improve patient outcomes and hospital resource management.

Purpose of the Study:

• To review recent predictive analytics models for heart failure risk, readmission, and mortality.
• To identify common modeling techniques and data sources used in heart failure prediction.
• To assess the potential of predictive analytics in clinical practice.

Main Methods:

• Literature review of studies on predictive analytics for heart failure.
• Analysis of common machine learning algorithms (e.g., random forest, logistic regression, neural networks, XGBoost).
• Examination of data sources, including electronic health records (structured and unstructured data) and preprocessing techniques (imputation, feature selection).

Main Results:

• Predictive analytics models demonstrate potential for early heart failure diagnosis.
• Models show promise in stratifying readmission risk and predicting mortality.
• Machine learning techniques are increasingly applied to heart failure prediction.

Conclusions:

• Predictive analytics, particularly machine learning, offers significant potential to improve heart failure outcomes.
• Further rigorous research and benchmarking are necessary for clinical adoption.
• Enhanced data utilization from electronic health records can advance heart failure management.