INTEGRATING BIG DATA ANALYTICS INTO HEALTHCARE DECISION SUPPORT SYSTEMS FOR BETTER PATIENT OUTCOMES

This article investigates the incorporation of BDA with healthcare DSSs to improve the quality of clinical decision-making and patient outcomes. This study aims to assess the potential of BDA in analyzing a broad range of healthcare data types, from EHRs to real-time sensor data and clinical histories, to aid in the prognostication of patient conditions, customized treatment, and Candice optimization. The approach deploys machine learning classifiers (ANN, RF, and SVM) on a synthetic dataset of over 500 thousand patient records. The findings show that the performance of the ANN model is better than the conventional machine learning models with an accuracy of 91.6%, precision of 0.89, recall of 0.87, and AUC-ROC of 0.92. Comparison with the current DSS models shows superior predictive precision, decision efficiency, and real-time intervention capacity. This incorporation of big data improves diagnostic and therapeutic results but also allows physicians to make real-time suggestions when making decisions, thereby allowing early clinical interventions. Significance This research contributes to realizing the transformative capabilities of BDA in the modernization of healthcare, the reduction of operation ineffectiveness, and patient services with a substantial impact.