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Rule Extraction for Screening of COVID-19 Disease Using Granular Computing Approach
Seyyed Meysam Rozehkhani1, Maryam Mohammadzad1
1Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran.
Insights
This study introduces a granular computing approach for early COVID-19 diagnosis using symptoms. The method effectively classifies patients, reducing unnecessary medical visits and improving healthcare access during pandemics.
Area of Science:
- Medical Informatics
- Computational Intelligence
- Epidemiology
Background:
- Inadequate access to treatment centers is a major challenge during viral epidemics like Coronavirus.
- Premature visits to medical facilities for mild symptoms contribute to virus spread and healthcare congestion.
- Early symptom detection is crucial for timely diagnosis and effective disease management.
Purpose of the Study:
- To present a novel approach for patient classification and disease diagnosis using granular computing.
- To address the challenge of early detection for unknown viral infections, specifically Coronavirus.
- To improve the efficiency of healthcare services by enabling accurate, symptom-based diagnosis.
Main Methods:
- Utilizing granular computing for patient classification and disease diagnosis based on symptoms.
- Employing expert-classified training datasets for rule extraction.
- Focusing on the extraction of rules with zero entropy for high accuracy.
Main Results:
- The proposed granular computing method successfully detects Coronavirus disease (COVID-19) based on observed symptoms.
- The approach facilitates accurate classification of patients, aiding in early diagnosis.
- Experimental results validate the effectiveness of the zero-entropy rule extraction.
Conclusions:
- Granular computing offers a powerful framework for symptom-based disease diagnosis.
- The developed method can aid in early detection of viral infections, mitigating epidemic spread.
- This approach has the potential to optimize healthcare resource allocation during public health crises.
Abstract:
In the epidemic status of an unknown virus called Coronavirus, one of the main problems is inadequate access to treatment centers. Statistics show that many people are infected with the virus through unseasonable visits to medical centers immediately after noticing the initial symptoms similar to those reported for Coronavirus. Besides, unnecessary congestion at health centers reduces the quality of service to patients in urgent need of care. Since any external factor, including the virus, appears to have some symptoms after the onset of activity in the affected person, early diagnosis is possible. This paper presents an approach to classifying patients and diagnosing disease by symptoms, based on granular computing. One of the vital features of this method is the extraction of correct rules with zero entropy. This process is done based on a predefined classification of training datasets collected by experts. Granular computing has been a helpful approach in rule extraction and variety in recent years. Experimental results show that the proposed method can successfully detect COVID-19 disease according to its observed symptoms.
