Fuzzy Cloud Based COVID-19 Diagnosis Assistant for identifying affected cases globally using MCDM

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Epidemiology

Background:

• Coronavirus Disease 2019 (COVID-19) has caused significant global mortality.
• Early detection, treatment, and awareness are crucial for containing COVID-19.
• Existing diagnostic methods require enhancement for real-time public health management.

Purpose of the Study:

• To propose a Fuzzy Cloud-Based (FCB) COVID-19 Diagnosis Assistant (DA).
• To accurately identify patients as confirmed, suspect, or suspicious for COVID-19.
• To categorize patient severity into mild, moderate, severe, or critical.

Main Methods:

• Real-time online registration of patients to create a dynamic database.
• Integration of a cloud-based platform with fuzzy logic for diagnostic analysis.
• Incorporation of expert medical teams for consultation and prevention strategies.

Main Results:

• The FCB COVID-19 DA improves diagnostic accuracy through real-world data updates.
• Enables real-time patient categorization and severity assessment.
• Facilitates enhanced consultation and prevention measures through integrated expert teams.

Conclusions:

• The proposed FCB COVID-19 DA offers a novel approach to managing the COVID-19 pandemic.
• Aims to de-accelerate transmission rates and improve patient outcomes.
• Highlights the potential of AI and cloud computing in public health crises.