Improving Predictive Healthcare Analytics with Sophisticated Machine Learning Solutions on the Cloudera Platform

Remote Patient Monitoring (RPM) is essential for proactive healthcare, particularly in chronic illness management and post-acute care. Predictive analytics improves RPM by detecting health problems early and personalizing treatments. Unified data architecture on the Cloudera Platform allows real-time intake, processing, and analytics across remote healthcare systems. Apache NiFi and Kafka gather streaming data from wearable devices, while Apache Spark and Cloudera Machine Learning transform and forecast. Cloudera Data Warehouse allows structured querying, while Cloudera DataFlow and Operational Database provide low-latency data access and alerts. HIPAA-compliant security and role-based governance protect sensitive patient data. The end-to-end architecture enables scalable, responsive, and intelligent RPM processes, improving clinical outcomes, hospital readmissions, and patient engagement via rapid data-driven insights. This technique has examined five Cloudera Platform variants: CDE, Cloudera Data Platform (CDP), Cloudera Data Warehouse (CDW), Cloudera Operational Database (COD), and Cloudera DataFlow (CDF). The Kaggle database displays three files with data from the Patient Side, Offline Doctor Survey, and Online Doctor Survey target audiences. This reveals that Cloudera Data Engineering (CDE) outperforms the other models. Sensitivity was 98.16 %, Specificity 98.59 %, Accuracy 98.37%, and F1- Score 98.37%.