International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 11, Issue 12 (December 2024), Pages: 225-231

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 Original Research Paper

Fuzzy logic and machine learning for diabetes risk prediction using modifiable factors

 Author(s): 

 Rabia Khushal *, Ubaida Fatima

 Affiliation(s):

 Department of Mathematics, NED University of Engineering and Technology Karachi, Karachi, Pakistan

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0009-0002-5437-3032

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.12.025

 Abstract

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.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Diabetes risk, Fuzzy logic, Modifiable factors, Machine learning, Lifestyle intervention

 Article history

 Received 28 August 2024, Received in revised form 14 November 2024, Accepted 29 November 2024

 Acknowledgment

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Khushal R and Fatima U (2024). Fuzzy logic and machine learning for diabetes risk prediction using modifiable factors. International Journal of Advanced and Applied Sciences, 11(12): 225-231

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 

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