A Comparative Analysis of Algorithmic Approaches to Predicting Patient Outcomes in Cardiovascular Care
Keywords:
cardiovascular disease, predictive analytics, machine learning, patient outcomes, algorithm comparison, heart failure, clinical decision supportAbstract
Cardiovascular disease remains a leading cause of mortality globally, necessitating accurate prediction models for patient outcomes. This study employs a comparative analysis of machine learning algorithms including Random Forest, Gradient Boosting, and Support Vector Machines in predicting 30-day readmission rates among heart failure patients. By utilizing a retrospective cohort of 5,000 patients, we deployed advanced statistical techniques and software tools such as Python 3.9 (scikit-learn 0.24.0) for algorithm implementation. Our findings reveal that the Gradient Boosting model outperformed other algorithms with an accuracy of 85% and a p-value of <0.01 in the statistical significance test. This research underscores the importance of leveraging advanced analytics in clinical settings to enhance patient management and resource allocation.
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