Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach
Background: AI-driven mental health solutions offer transformative potential for improving mental healthcare outcomes, but identifying the most effective approaches remains a challenge. This study addresses this gap by evaluating and prioritizing AI-driven mental health alternatives based on key cri...
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MDPI AG
2025-01-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/1/14 |
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| author | Yewande Ojo Olasumbo Ayodeji Makinde Oluwabukunmi Victor Babatunde Gbotemi Babatunde Subomi Okeowo |
| author_facet | Yewande Ojo Olasumbo Ayodeji Makinde Oluwabukunmi Victor Babatunde Gbotemi Babatunde Subomi Okeowo |
| author_sort | Yewande Ojo |
| collection | DOAJ |
| description | Background: AI-driven mental health solutions offer transformative potential for improving mental healthcare outcomes, but identifying the most effective approaches remains a challenge. This study addresses this gap by evaluating and prioritizing AI-driven mental health alternatives based on key criteria, including feasibility of implementation, cost-effectiveness, scalability, ethical compliance, user satisfaction, and impact on clinical outcomes. Methods: A fuzzy multi-criteria decision-making (MCDM) model, consisting of fuzzy TOPSIS and fuzzy ARAS, was employed to rank the alternatives, while a hybridization of the two methods was used to address discrepancies between the methods, each emphasizing distinct evaluative aspect. Results: Fuzzy TOPSIS, focusing on closeness to the ideal solution, ranked personalization of care (A5) as the top alternative with a closeness coefficient of 0.50, followed by user engagement (A2) at 0.45. Fuzzy ARAS, which evaluates cumulative performance, also ranked A5 the highest, with an overall performance rating of Si = 0.90 and utility degree Qi = 0.92. Combining both methods provided a balanced assessment, with A5 retaining its top position due to high scores in user satisfaction and clinical outcomes. Conclusions: This result underscores the importance of personalization and engagement in optimizing AI-driven mental health solutions, suggesting that tailored, user-focused approaches are pivotal for maximizing treatment success and user adherence. |
| format | Article |
| id | doaj-art-6234304565f04ba9ac5cc41ec81097a9 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | AI |
| spelling | doaj-art-6234304565f04ba9ac5cc41ec81097a92025-01-24T13:17:23ZengMDPI AGAI2673-26882025-01-01611410.3390/ai6010014Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making ApproachYewande Ojo0Olasumbo Ayodeji Makinde1Oluwabukunmi Victor Babatunde2Gbotemi Babatunde3Subomi Okeowo4Department of Communications Management, University of Denver, Denver, CO 80208, USADepartment of Quality and Operations Management, University of Johannesburg, Johannesburg 2028, South AfricaDepartment of Industrial Design, Federal University of Technology, Akure 340110, NigeriaGraduate School of Professional Psychology, University of Denver, Denver, CO 80208, USACollege of Arts, Media and Design, Northeastern University, Boston, MA 02115, USABackground: AI-driven mental health solutions offer transformative potential for improving mental healthcare outcomes, but identifying the most effective approaches remains a challenge. This study addresses this gap by evaluating and prioritizing AI-driven mental health alternatives based on key criteria, including feasibility of implementation, cost-effectiveness, scalability, ethical compliance, user satisfaction, and impact on clinical outcomes. Methods: A fuzzy multi-criteria decision-making (MCDM) model, consisting of fuzzy TOPSIS and fuzzy ARAS, was employed to rank the alternatives, while a hybridization of the two methods was used to address discrepancies between the methods, each emphasizing distinct evaluative aspect. Results: Fuzzy TOPSIS, focusing on closeness to the ideal solution, ranked personalization of care (A5) as the top alternative with a closeness coefficient of 0.50, followed by user engagement (A2) at 0.45. Fuzzy ARAS, which evaluates cumulative performance, also ranked A5 the highest, with an overall performance rating of Si = 0.90 and utility degree Qi = 0.92. Combining both methods provided a balanced assessment, with A5 retaining its top position due to high scores in user satisfaction and clinical outcomes. Conclusions: This result underscores the importance of personalization and engagement in optimizing AI-driven mental health solutions, suggesting that tailored, user-focused approaches are pivotal for maximizing treatment success and user adherence.https://www.mdpi.com/2673-2688/6/1/14AI-driven mental health solutionshybrid fuzzy multi-criteria decision-making (MCDM)fuzzy TOPSIS and fuzzy ARAS methodspersonalization of careuser engagement |
| spellingShingle | Yewande Ojo Olasumbo Ayodeji Makinde Oluwabukunmi Victor Babatunde Gbotemi Babatunde Subomi Okeowo Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach AI AI-driven mental health solutions hybrid fuzzy multi-criteria decision-making (MCDM) fuzzy TOPSIS and fuzzy ARAS methods personalization of care user engagement |
| title | Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach |
| title_full | Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach |
| title_fullStr | Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach |
| title_full_unstemmed | Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach |
| title_short | Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach |
| title_sort | evaluating ai driven mental health solutions a hybrid fuzzy multi criteria decision making approach |
| topic | AI-driven mental health solutions hybrid fuzzy multi-criteria decision-making (MCDM) fuzzy TOPSIS and fuzzy ARAS methods personalization of care user engagement |
| url | https://www.mdpi.com/2673-2688/6/1/14 |
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