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Cloud-based real-time enhancement for disease prediction using Confluent Cloud, Apache Kafka, feature optimization,

Abdulaziz AlMohimeed1

  • 1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary

This study introduces a real-time system for early chronic kidney disease (CKD) detection using Internet of Things (IoT) data. The system integrates ensemble models, Explainable AI (XAI), and feature selection for accurate, real-time health monitoring.

Keywords:
Apache KafkaConfluent CloudFeature optimization explainable artificial intelligenceMachine learningStacking modelStream processing platforms

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Area of Science:

  • Healthcare Technology
  • Artificial Intelligence in Medicine
  • Big Data Analytics

Background:

  • Internet of Things (IoT) is generating vast healthcare data.
  • Predictive real-time systems require advanced data analysis.
  • Early detection of chronic kidney disease (CKD) improves patient outcomes.

Purpose of the Study:

  • To develop a real-time system for early CKD detection and treatment.
  • To integrate ensemble models, Explainable AI (XAI), and feature selection (FS) for predictive healthcare.
  • To leverage big data streaming platforms for real-time health monitoring.

Main Methods:

  • A two-phase approach was used, involving stacking models and feature selection (Genetic Algorithm - GA, Particle Swarm Optimization - PSO).
  • Explainable AI (XAI) was applied to the best performing model.
  • A real-time streaming pipeline was built using Confluent Cloud and Apache Kafka with Python scripts.

Main Results:

  • The stacking model with GA-selected features achieved 100% accuracy, precision, recall, and F1-score in phase one.
  • The real-time pipeline demonstrated the stacking model's effectiveness with 100% accuracy for CKD prediction.
  • The system successfully processed streaming health data for real-time analysis.

Conclusions:

  • The developed real-time system effectively detects CKD early using integrated AI and big data technologies.
  • The combination of stacking models, GA for feature selection, and XAI provides a robust solution for predictive healthcare.
  • Confluent Cloud and Apache Kafka enable efficient real-time data streaming for healthcare applications.