AI simulation models for diagnosing disabilities in smart electrical prosthetics using bipolar fuzzy decision making based on choquet integral

• Published in: Scientific reports Vol. 15; no. 1; pp. 29244 - 19
• Main Authors: Rehman, Ubaid ur, Khan, Meraj Ali, Aldayel, Osamah AbdulAziz, Mahmood, Tahir
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.08.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Accuracy; Algorithms; Artificial Intelligence; Case studies; Choquet integral; Computer Simulation; Cost control; Decision Making; Design; Disability; Disease; Electrical prosthetic systems; Fuzzy Logic; Fuzzy sets; Humanities and Social Sciences; Humans; Information processing; Machine learning; MCDM methodology; Medical personnel; multidisciplinary; Persons with Disabilities - rehabilitation; Prostheses; Prostheses and Implants; Prosthetics; Science; Science (multidisciplinary); Simulation; User behavior; Disability; MCDM methodology; Choquet integral; Artificial intelligence; Electrical prosthetic systems
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-12267-1

The integration of AI simulation models within smart electrical prosthetic systems represents a significant advancement in disability disease diagnosis. However, the selection and evaluation of these AI models interpret some multi-criteria decision-making dilemmas because of the presence of uncertainty and bipolarity (positive and negative aspects) of the selection criteria. Current literature lacks the selection and evaluation of AI simulation models that consider both bipolarity and uncertainty of the criteria, while prevailing Choquet integral aggregation operators despite their strong capabilities for handling information relationships, fail to efficiently process bipolar fuzzy information. The existence of this limitation makes it challenging to identify element interactions and non-linear relationships in uncertain environments containing both positive and negative aspects. To overcome these gaps, first, we develop two operators that are the bipolar fuzzy Choquet integral averaging and bipolar fuzzy Choquet integral geometric operators that uniquely integrate dual aspects (bipolarity) with criterion interaction modeling capabilities, fundamentally differing from traditional fuzzy approaches that cannot simultaneously process dual aspects of criterion. Secondly, we design a new multi-criteria decision-making approach using these operators to assess AI simulation models for prosthetic systems, since the criteria involved such as diagnostic accuracy, computational efficiency, and system reliability, have both positive and negative aspects that need to be considered together. Our method was applied in detail to select AI simulation models for smart electrical prosthetic systems and compared with some prevailing methods and standard Choquet integral approaches. This showed that our method is more precise and produces better evaluation results. It introduces a new theoretical basis for bipolar fuzzy Choquet integral aggregation and offers medical professionals a reliable way to pick the best AI simulation models for important prosthetic applications that influence patient outcomes and the functioning of prosthetics.