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

Abstract

Heart failure is a leading cause of death among people worldwide. The cost of treatment can be prohibitive, and early prediction of heart failure would reduce treatment costs to patients and hospitals. Improved readmission prediction would also greatly help hospitals, allowing them to manage their treatment programs and budgets better. This literature review aims to summarize recent studies of predictive analytics models that have been constructed to predict heart failure risk, readmission, and mortality. Random forest, logistic regression, neural networks, and XGBoost were among the most common modeling techniques applied. Most selected studies leveraged structured electronic health record data, including demographics, clinical values, lifestyle, and comorbidities, with some incorporating unstructured clinical notes. Preprocessing through imputation and feature selection were frequently employed in building the predictive analytics models. The reviewed studies exhibit demonstrated promise for predictive analytics in improving early heart failure diagnosis, readmission risk stratification, and mortality prediction. This review study highlights rising research activities and the potential of predictive analytics, especially the implementation of machine learning, in advancing heart failure outcomes. Further rigorous, comprehensive syntheses and head-to-head benchmarking of predictive models are needed to derive robust evidence for clinical adoption.

Keywords: heart failure; mortality; predictive analytics; predictive models; readmission; risk prediction.

Figure 1. Literature review flow diagram

Figure 2. Machine learning algorithms used in selected research articles

SVM:Support Vector Machine; XGBoost:eXtreme Gradient Boosting; MLP:Multilayer Perceptron

Figure 3. Data categories reported in the selected papers

NYHA:New York Heart Association; LoS:Length of Stay

Figure 4. Publication by year based on refined-search results

Figure 5. Methodology used in the refined search papers

The medical approach, commonly used by medical field experts like doctors and clinicians in presenting their predictive model by using medical or clinical approaches like physical examination and laboratory tests. The statistical approach or statistical analysis approach including Cox hazard proportional approach. The machine learning approach, the artificial intelligence-based approach. The big data and data mining approach, where researchers use various big data applications or software in building predictive models.