Detecting Patterns of Infection-Induced Fertility Using Fermatean Neutrosophic Set With Similarity Analysis
oleh: Muhammad Saeed, Mehar Un Nisa, Muhammad Haris Saeed, Tmader Alballa, Hamiden Abd El-Wahed Khalifa
| Format: | Article |
|---|---|
| Diterbitkan: | IEEE 2023-01-01 |
Deskripsi
Urinary tract infections (<inline-formula> <tex-math notation="LaTeX">$\mathbb {UTI}s$ </tex-math></inline-formula>) pose a significant challenge globally, as they increase the risk of miscarriage and promote the growth of gram-negative bacteria. Accurately assessing susceptibility is crucial for effective diagnosis and treatment in resource-limited settings. In the field of diagnosing and treating infected patients, numerous models have been suggested in various studies. An innovative mathematical model is presented for analysing <inline-formula> <tex-math notation="LaTeX">$\mathbb {UTI}s$ </tex-math></inline-formula>. To address this disease, a decision-making model is developed utilizing the Fermatean Neutrosophic set (<inline-formula> <tex-math notation="LaTeX">$\mathbb {F_{N}S}$ </tex-math></inline-formula>) distance and similarity measures (<inline-formula> <tex-math notation="LaTeX">$\mathbb {SM}$ </tex-math></inline-formula>), which is the affixed structure of the Pythagorean neutrosophic set (<inline-formula> <tex-math notation="LaTeX">$\mathbb {P_{N}S}$ </tex-math></inline-formula>) and the intuitionistic neutrosophic set (<inline-formula> <tex-math notation="LaTeX">$\mathbb {I_{N}S}$ </tex-math></inline-formula>). The <inline-formula> <tex-math notation="LaTeX">$\mathbb {F_{N}S}$ </tex-math></inline-formula> susceptibility model incorporates expert opinions to identify appropriate types of <inline-formula> <tex-math notation="LaTeX">$\mathbb {UTI}s$ </tex-math></inline-formula> based on relevant symptoms or parameters. It calculates the distance and similarity between an ideal <inline-formula> <tex-math notation="LaTeX">$\mathbb {UTI}s$ </tex-math></inline-formula> patient and the <inline-formula> <tex-math notation="LaTeX">$\mathbb {F_{N}S}$ </tex-math></inline-formula> for the disease. This approach aims to provide a robust multi-attribute selection support mechanism, minimizing biases and errors associated with subjective evaluations. However, it’s important to note that this method does not replace traditional diagnostic techniques. It should be used alongside other ways for the most accurate diagnosis. Implementing this model can improve the management of <inline-formula> <tex-math notation="LaTeX">$\mathbb {UTI}s$ </tex-math></inline-formula>, ultimately enhancing overall population health. A table is constructed for <inline-formula> <tex-math notation="LaTeX">$\mathbb {UTI}s$ </tex-math></inline-formula> patients using a fuzzy interval of [0, 1]. The calculation involves distance and similarity measures within the Fermatean neutrosophic <inline-formula> <tex-math notation="LaTeX">$\mathbb {F_{N}}$ </tex-math></inline-formula> environment, enabling accurate <inline-formula> <tex-math notation="LaTeX">$\mathbb {UTI}s$ </tex-math></inline-formula> diagnosis. <inline-formula> <tex-math notation="LaTeX">$\mathbb {F_{N}S}$ </tex-math></inline-formula> provide a robust framework for modelling uncertainty in <inline-formula> <tex-math notation="LaTeX">$\mathbb {UTI}s$ </tex-math></inline-formula> diagnosis. It can represent indeterminacy, inconsistency, and incomplete information, which are common in medical data.