Type-2 Gaussian neuro-fuzzy VIKOR technique in multi-criteria decision-making for medical diagnostic
Nivedita, Seema Agrawal, Meenakshi Sharma, Laxmi Rathour, Vishnu Narayan Mishra · 2024
In real-world computation, complex and imprecise data are prevalent. The digital world generates a huge amount of data across various fields such as business, agriculture, banking, defence, and education. The medical field, especially during the emergence of COVID-19, faces the challenges of diagnosing specific diseases among a multitude of evolving symptoms. Identifying the exact disease becomes a tedious task as patients may exhibit similar symptoms for multiple diseases. This present work aims for the formation of a comprehensive system based on the type-2 Gaussian neuro-fuzzy VIKOR method. The proposed method utilizes multi-criteria decision-making (MCDM) and constructs a new dimension of the VIKOR method using type-2 Gaussian neuro-fuzzy numbers. The system incorporates fuzzy ranking for alternative ordering and employs the method of numerical computation to validate its efficiency and validity. The model is applied to different diseases with common symptoms, such as lung cancer, pneumonia, asthma, bronchitis, and tuberculosis, with a comparative study conducted as part of this research. A relative study of the projected method by using the extension of fuzzy sets is also carried out.