Identification of mental disorders in South Africa using complex probabilistic hesitant fuzzy N-soft aggregation information
Abstract This paper aims to address the challenges faced by medical professionals in identifying mental disorders. These mental health issues are an increasing public health concern, and middle-income nations like South Africa are negatively impacted. Mental health issues pose a substantial public h...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-45991-7 |
| _version_ | 1797576889281282048 |
|---|---|
| author | Shahzaib Ashraf Muneeba Kousar Gilbert Chambashi |
| author_facet | Shahzaib Ashraf Muneeba Kousar Gilbert Chambashi |
| author_sort | Shahzaib Ashraf |
| collection | DOAJ |
| description | Abstract This paper aims to address the challenges faced by medical professionals in identifying mental disorders. These mental health issues are an increasing public health concern, and middle-income nations like South Africa are negatively impacted. Mental health issues pose a substantial public health concern in South Africa, putting forth extensive impacts on both individuals and society broadly. Insufficient funding for mental health remains the greatest barrier in this country. In order to meet the diverse and complex requirements of patients effective decision making in the treatment of mental disorders is crucial. For this purpose, we introduced the novel concept of the complex probabilistic hesitant fuzzy N-soft set (CPHFNSS) for modeling the unpredictability and uncertainty effectively. Our approach improves the precision with which certain traits connected to different types of mental conditions are recognized by using the competence of experts. We developed the fundamental operations (like extended and restricted intersection, extended and restricted union, weak, top, and bottom weak complements) with examples. We also developed the aggregation operators and their many features, along with their proofs and theorems, for CPHFNSS. By implementing these operators in the aggregation process, one could choose a combination of characteristics. Further, we introduced the novel score function, which is used to determine the optimal choice among them. In addition, we created an algorithm with numerical illustrations for decision making in which physicians employ CPHFNS data to diagnose a specific condition. Finally, comparative analyses confirm the practicability and efficacy of the technique that arises from the model developed in this paper. |
| first_indexed | 2024-03-10T22:01:10Z |
| format | Article |
| id | doaj.art-2b596fd58a4a4fab8ea484ef9dbfb65c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T22:01:10Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-2b596fd58a4a4fab8ea484ef9dbfb65c2023-11-19T12:57:24ZengNature PortfolioScientific Reports2045-23222023-11-0113112610.1038/s41598-023-45991-7Identification of mental disorders in South Africa using complex probabilistic hesitant fuzzy N-soft aggregation informationShahzaib Ashraf0Muneeba Kousar1Gilbert Chambashi2Institute of Mathematics, Khwaja Fareed University of Engineering & Information TechnologyInstitute of Mathematics, Khwaja Fareed University of Engineering & Information TechnologySchool of Business Studies, Unicaf UniversityAbstract This paper aims to address the challenges faced by medical professionals in identifying mental disorders. These mental health issues are an increasing public health concern, and middle-income nations like South Africa are negatively impacted. Mental health issues pose a substantial public health concern in South Africa, putting forth extensive impacts on both individuals and society broadly. Insufficient funding for mental health remains the greatest barrier in this country. In order to meet the diverse and complex requirements of patients effective decision making in the treatment of mental disorders is crucial. For this purpose, we introduced the novel concept of the complex probabilistic hesitant fuzzy N-soft set (CPHFNSS) for modeling the unpredictability and uncertainty effectively. Our approach improves the precision with which certain traits connected to different types of mental conditions are recognized by using the competence of experts. We developed the fundamental operations (like extended and restricted intersection, extended and restricted union, weak, top, and bottom weak complements) with examples. We also developed the aggregation operators and their many features, along with their proofs and theorems, for CPHFNSS. By implementing these operators in the aggregation process, one could choose a combination of characteristics. Further, we introduced the novel score function, which is used to determine the optimal choice among them. In addition, we created an algorithm with numerical illustrations for decision making in which physicians employ CPHFNS data to diagnose a specific condition. Finally, comparative analyses confirm the practicability and efficacy of the technique that arises from the model developed in this paper.https://doi.org/10.1038/s41598-023-45991-7 |
| spellingShingle | Shahzaib Ashraf Muneeba Kousar Gilbert Chambashi Identification of mental disorders in South Africa using complex probabilistic hesitant fuzzy N-soft aggregation information Scientific Reports |
| title | Identification of mental disorders in South Africa using complex probabilistic hesitant fuzzy N-soft aggregation information |
| title_full | Identification of mental disorders in South Africa using complex probabilistic hesitant fuzzy N-soft aggregation information |
| title_fullStr | Identification of mental disorders in South Africa using complex probabilistic hesitant fuzzy N-soft aggregation information |
| title_full_unstemmed | Identification of mental disorders in South Africa using complex probabilistic hesitant fuzzy N-soft aggregation information |
| title_short | Identification of mental disorders in South Africa using complex probabilistic hesitant fuzzy N-soft aggregation information |
| title_sort | identification of mental disorders in south africa using complex probabilistic hesitant fuzzy n soft aggregation information |
| url | https://doi.org/10.1038/s41598-023-45991-7 |
| work_keys_str_mv | AT shahzaibashraf identificationofmentaldisordersinsouthafricausingcomplexprobabilistichesitantfuzzynsoftaggregationinformation AT muneebakousar identificationofmentaldisordersinsouthafricausingcomplexprobabilistichesitantfuzzynsoftaggregationinformation AT gilbertchambashi identificationofmentaldisordersinsouthafricausingcomplexprobabilistichesitantfuzzynsoftaggregationinformation |