Quantifying Multi-Cause Psychological Disorder Risk Through an Advanced Mathematical Model Using Intuitionistic Pentagonal Fuzzy Logic

Aiman Ishtiaq; Khadija Tul Kubra; Anns Uzair; Awais Ali

doi:10.31181/sor41202762

Authors

Aiman Ishtiaq Government College University Faisalabad, Pakistan Author https://orcid.org/0009-0005-1538-2907
Khadija Tul Kubra Government College University Faisalabad, Pakistan Author https://orcid.org/0000-0001-5716-8048
Anns Uzair Government College University Faisalabad, Pakistan Author https://orcid.org/0009-0007-3703-0384
Awais Ali Government College University Faisalabad, Pakistan Author https://orcid.org/0009-0000-3955-3938

DOI:

https://doi.org/10.31181/sor41202762

Keywords:

Multi-Criteria Decision Making, Intuitionistic Pentagonal Fuzzy Framework, Fuzzy Logical operators, Psychiatric Disorder Evaluation

Abstract

The evaluation of psychological disorders is a very complicated and sensitive area of study, where emotions, thoughts, and the mental states of different patients are analyzed on the basis of questionnaires and observational techniques. The identification and analysis of people's mental states, emotions, and behaviors are difficult and limited with the aid of conventional approaches since the data gathered from these factors appear to be vague, uncertain, and imprecise. To demonstrate this information systematically, we have proposed a comprehensive mathematical model able to deal with indeterminate and imprecise data, utilizing Intuitionistic Pentagonal Fuzzy Numbers (IPnFNs). To create a coherent mathematical framework, IPnFNs integrate five values of membership and non-membership functions of any constraint, providing an innovative numerical framework that effectively explains the assessment of diagnosis and the patient's condition. The research study analyzes several key psychiatric disorders, such as childhood psychiatric problems, hormonal changes, social stress, cognitive distortions, anxiety, and traumatic life events, as the main factors. The proposed technique provides psychologists with a thorough and clear picture of the issue by depicting the patient's mental health numerically. The research is significantly helpful in cases where the symptoms of psychological illness are not obvious or the patient's information is imprecise or confusing. Furthermore, a comparative analysis of various patients can be developed using the proposed technique, assisting in assessing severity and diagnosis preferences. This mathematical method shows that the use of IPnFNs can be a reliable and scientific method in the evaluation of psychological disorders. This approach is significantly applicable in educational counseling and clinical psychology, where it is a daily routine for individuals to deal with imprecise and inconsistent data in mental health issues while making decisions.

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