| Autor: |
Tarun Kumar, Anirudh Kumar Bhargava, M.K. Sharma, Nitesh Dhiman, Neha Nain |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Clinical eHealth, Vol 7, Iss , Pp 15-26 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2588-9141 |
| DOI: |
10.1016/j.ceh.2024.01.001 |
| Popis: |
This research work presents a hybrid approach combining a type-2 fuzzy inference system with particle swarm optimization (PSO) to develop a type-2 fuzzy optimized inference system, specifically tailored for asthma patient data. Addressing the inherent uncertainty in medical diagnostics, this model enhances traditional type-1 fuzzy logic by incorporating ambiguity into linguistic variables and utilizing type-2 fuzzy if-then rules. The system is trained to minimize diagnostic error in asthma disease identification. Applied to a dataset comprising eight medical entities from asthma patients, the model demonstrates substantial accuracy improvements. Numerical computations validate the system, showing a decrease in error rate from 1.445 to 0.03, indicating a significant enhancement in diagnostic precision. These results underscore the potential of our model in medical diagnostic problems, providing a novel and effective tool for tackling the complexities of asthma diagnosis. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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