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Predictive Model for Smart Healthcare Systems Using Random Forest Classifier

Author

Listed:
  • B. I. Ele

    (Department of Computer Science, University of Calabar, Calabar, Nigeria)

  • E. O. Omini

    (Department of Computer Science, University of Calabar, Calabar, Nigeria)

  • O. O. Obu

    (Department of Computer Science, University of Calabar, Calabar, Nigeria)

  • C. P. Isong

    (College of Health Sciences Management and Technology, Calabar, Nigeria)

  • D. E. Izuki

    (Directorate of Information and Communication Technology, University of Cross River State, Calabar, Nigeria)

Abstract

Health care systems in developing countries often face serious challenges, including limited resources, poor infrastructure, and delays in patient care. This study presents the development of a predictive model designed to assist in early health risk detection, particularly in resource-constrained settings. In this study, an improved predictive model for smart healthcare systems using Random Forest Classifier was created and embedded in a simple web interface. The model was trained on synthetic medical data and achieved an accuracy of 91% during testing. Health workers and others were able to use the system effectively, even with minimal digital skills. The platform provided real-time predictions, that will help users make quicker clinical decisions.

Suggested Citation

  • B. I. Ele & E. O. Omini & O. O. Obu & C. P. Isong & D. E. Izuki, 2026. "Predictive Model for Smart Healthcare Systems Using Random Forest Classifier," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 15(6), pages 1067-1073, July.
    • Handle: RePEc:bjf:ijltem:v:15:y:2026:i:6:a:235
    DOI: 10.51583/IJLTEMAS.2026.150600076
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