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Related Experiment Video

Updated: Aug 5, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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IoT-based disease prediction using machine learning.

Salman Ahmad Siddiqui1, Anwar Ahmad1, Neda Fatima1

  • 1Department of Electronics and Communication Engineering, Jamia Millia Islamia, New Delhi, India.

Computers & Electrical Engineering : an International Journal
|March 29, 2023
PubMed
Summary

This study introduces a novel Internet of Things (IoT) system using machine learning (ML) to predict diseases from patient data. The system aims to enhance remote healthcare by analyzing symptoms and medical history for accurate diagnosis and treatment recommendations.

Keywords:
Disease detectionDisease predictionInternet of thingsMachine learning

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Internet of Things (IoT)

Background:

  • The COVID-19 pandemic significantly strained healthcare systems globally.
  • Increased patient numbers necessitated remote healthcare solutions like telemedicine and virtual consultations.
  • The need for efficient remote patient monitoring and diagnosis is critical in the post-COVID era.

Purpose of the Study:

  • To develop a novel system leveraging the Internet of Things (IoT) for disease prediction.
  • To implement an efficient machine learning (ML) algorithm for accurate patient diagnosis.
  • To create a platform for remote healthcare, enhancing accessibility and efficiency.

Main Methods:

  • Utilizing patient-provided data including symptoms, audio recordings, medical reports, and illness history.
  • Integrating sensors (Arduino, ESP8266) for real-time measurement of symptoms like fever and blood oxygen.
  • Employing a machine learning (ML) algorithm for holistic disease prediction and diagnosis.

Main Results:

  • The proposed system accurately predicts diseases based on comprehensive patient data analysis.
  • The integration of IoT sensors allows for objective measurement of key health indicators.
  • The system provides appropriate diagnosis and treatment recommendations from an updated database.

Conclusions:

  • The developed IoT-based ML system offers a promising solution for remote disease prediction and diagnosis.
  • This technology can significantly support healthcare providers in managing patient load and improving care.
  • The platform, as an application or website, can enhance healthcare accessibility and efficiency in remote settings.