IoT-based disease prediction using machine learning.

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.