Infectious Diseases Diagnosis and Treatment Suggestions Using Complex Neutrosophic Hypersoft Mapping
Infectious diseases are one of the leading causes of death all over the world. This study aims to define the debates around the diagnosis of infectious diseases and their associated issues. After looking at the side effects of Infectious Diseases, it becomes difficult to distinguish the types of Inf...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/be53050cdcf1407f8f71ed84b783fc7a |
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| Sumario: | Infectious diseases are one of the leading causes of death all over the world. This study aims to define the debates around the diagnosis of infectious diseases and their associated issues. After looking at the side effects of Infectious Diseases, it becomes difficult to distinguish the types of Infectious Diseases and their severities. It is difficult to detect the efficiency in treating a patient record and predicting the length of medicine because the indeterminacy, false components, amplitude term (A-term), phase term (P-term), and sub parametric values are commonly rejected in terms of practical evaluation. This paper introduces the Complex Neutrosophic Hypersoft (CNHS) set and CNHS mapping with its inverse mapping to overcome these limitations. This theory will be more flexible in three ways. First, it includes indeterminacy and falsity components, which will utilise parametric values to analyse data in all three dimensions of the patient’s illness: positive, indeterminant, and negative. Secondly, for easier understanding, it separates the various attributes into distinct attribute-valued sets. Third, it provides for a large range of membership function values by expanding membership to the unit circle on an Argand plane and introducing an additional term known as the P-term to account for the periodic character of the data. To correctly analyse the problem, these principles can be coupled with scientific modelling. This study demonstrates a correlation between symptoms and treatments. A table with a fuzzily defined gap between 0 and 1 is created for different types of infectious diseases. The computation is based on CNHS mapping in order to properly detect the condition and select the appropriate prescription for each patient’s ailment. Eventually, a generalised CNHS mapping is offered, which can assist a doctor in releasing the chronology of the patient’s health status and predicting the time frame of therapy until the sickness is cleared. |
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