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Early infectious diseases identification based on complex probabilistic hesitant fuzzy N-soft information
Shahzaib Ashraf1, Muneeba Kousar1, Muhammad Shazib Hameed1
1Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200 Pakistan.
Abstract:
This paper aims to assess and deal with the challenges experienced by medical professionals caring for infectious diseases. In Pakistan, public health is still a serious concern and the main contributor to morbidity and mortality is infectious diseases. The major issue is a resemblance in the clinical symptoms of infectious diseases such as tuberculosis, hepatitis, COVID-19, dengue, and malaria. Early detection of infectious disease is crucial in order to start treatment with counseling and medication. This can only be done if several infections with similar clinical traits can be diagnosed depending on several criteria, including the availability of various kits, the ability to carry out diagnostic procedures, money, and technical staff. But woefully Pakistan's economy is badly battered due to several circumstances. Therefore, we are unable to provide patients with enough diagnostic testing kits and broadly accessible therapy choices, which makes diagnosis more difficult and create hesitancy with fuzziness and randomness. For this purpose, we introduced the new concept of the complex probabilistic hesitant fuzzy N-soft set. We defined its fundamental operations (like restricted and extended union, restricted and extended intersection, weak, top and bottom weak complements, as well as soft max-AND or soft min-OR) with examples. We also discussed their many properties with their proofs and theorems. Furthermore, we developed the algorithms for decision-making where doctors use the complex probabilistic hesitant fuzzy N-soft information to identify a particular disease. Furthermore, we explained numerical illustration of two case studies. Moreover, a sensitive and comparative analysis is discussed. In the last, we conclude the whole study.
Insights
Diagnosing infectious diseases in Pakistan is challenging due to similar symptoms and economic constraints. A new complex probabilistic hesitant fuzzy N-soft set approach aids medical professionals in accurate disease identification and decision-making.
Area of Science:
- Medical informatics
- Decision science
- Fuzzy set theory
Background:
- Infectious diseases pose a significant public health challenge in Pakistan, contributing to high morbidity and mortality.
- Similar clinical presentations of diseases like tuberculosis, hepatitis, COVID-19, dengue, and malaria complicate accurate diagnosis.
- Economic limitations in Pakistan hinder access to essential diagnostic kits and treatment options, exacerbating diagnostic difficulties.
Purpose of the Study:
- To address the diagnostic challenges faced by medical professionals in Pakistan concerning infectious diseases.
- To introduce a novel mathematical framework for improved disease identification and decision-making in resource-limited settings.
- To develop and validate a new decision-making algorithm utilizing complex probabilistic hesitant fuzzy N-soft sets for medical diagnosis.
Main Methods:
- Introduction and definition of the complex probabilistic hesitant fuzzy N-soft set and its fundamental operations (union, intersection, complements, soft logical operators).
- Exploration of the theoretical properties, theorems, and proofs associated with the proposed N-soft set structure.
- Development of decision-making algorithms designed for medical professionals to interpret complex probabilistic hesitant fuzzy N-soft information for disease diagnosis.
Main Results:
- The study defines novel operations and properties for complex probabilistic hesitant fuzzy N-soft sets, providing a robust mathematical foundation.
- Algorithms were developed to facilitate disease identification using the proposed N-soft set framework, aiding clinical decision-making.
- Numerical illustrations and comparative analyses demonstrated the practical applicability and sensitivity of the developed approach in case studies.
Conclusions:
- The complex probabilistic hesitant fuzzy N-soft set offers a promising tool to overcome diagnostic uncertainties in infectious disease management.
- The developed algorithms can assist healthcare providers in Pakistan and similar settings to make more informed and accurate diagnostic decisions.
- This research contributes a novel methodology to medical informatics and decision science for tackling complex diagnostic challenges in public health.
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