Enhancing Healthcare Outcomes Through Big Data Analytics Using Advanced Data Mining and Classification Techniques
Keywords:
Big Data Analytics, Healthcare, Random Forest, Patient Readmission, Classification, Predictive Modeling.Abstract
Big data analytics is transforming the healthcare industry by enabling deeper insights through data mining and classification techniques. These technologies improve decision-making, diagnosis, and overall healthcare outcomes by analyzing vast volumes of medical data. However, existing methods often struggle with the accuracy, scalability, and handling of high-dimensional healthcare datasets, leading to limited predictive performance and inefficient clinical interventions. To address these challenges, this study proposes a framework called Random Forest Classification-based Predicting Patient Readmission Risk (RFC-PPRR). This framework leverages the ensemble learning capability of Random Forest to effectively process large electronic health record (EHR) datasets and accurately predict patient readmission risk within 30 days of discharge. The proposed method enables hospitals to identify high-risk patients early, facilitating the implementation of preventive measures and more effective resource allocation. An experimental evaluation of real-world healthcare datasets demonstrated improved prediction accuracy, reduced false positives, and enhanced model interpretability compared to traditional classification methods. These outcomes suggest that RFC-PPRR can significantly contribute to reducing avoidable readmissions and optimizing patient care strategies.
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