7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
https://doi.org/10.21833/ijaas.2024.12.025
Copy DOIPublication Date: Dec 1, 2024 |
Diabetes mellitus, a global health concern, includes type 1 diabetes, with an uncontrollable risk, and type 2 diabetes, where risk can be managed through lifestyle modifications. This study examines the impact of modifiable risk factors—diet, physical activity, and body mass index (BMI)—on type 2 diabetes development. Using fuzzy logic, binary variables from a healthcare diabetes dataset were transformed into a fuzzy format, generating three output classes: "no diabetes risk," "possible diabetes risk," and "diabetes risk present." The intermediate class, "possible diabetes risk," serves as an alert for adopting healthier lifestyles to mitigate risk. Machine learning was applied to both the original and fuzzy-transformed datasets. While the original dataset provided binary outputs with moderate accuracy and higher computation times, the fuzzy-transformed dataset yielded more nuanced predictions, reduced computation time, and improved classifier performance. This approach enhances diabetes risk assessment and supports proactive interventions.
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.