Abstract
Nowadays, improvements in diabetes detection that provide patients with vital information are needed. This is due to the fact that Diabetes mellitus has generated a worldwide epidemic that costs society and people. Also, patients tend to misread symptoms, and clinicians who collect insufficient data may produce erroneous outcomes. Therefore, this study aims to demonstrate that a programme that integrates expert advice such as decisions, recommendations, or solutions is an excellent method for reducing the incidence of diabetes. Specifically, this study intends to implement a fuzzy expert system that can detect and report the early stages of diabetes as a viable approach. Furthermore, since this programme is available to everyone, people may easily self-diagnose themselves if they have a blood glucose monitoring device. However, developing the fuzzy expert system for real-world situations, such as diabetes patients, using any programming tools is not straightforward. Therefore, this study will provide a comprehensive approach to constructing a fuzzy expert system using the popular programming language Python.
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CITATION STYLE
Razak, T. R., Ul-Saufie, A. Z., Yusoff, M. H., Ismail, M. H., Fauzi, S. S. M., & Zaki, N. A. M. (2024). Python scikit-fuzzy: developing a fuzzy expert system for diabetes diagnosis. IAES International Journal of Artificial Intelligence, 13(2), 1398–1407. https://doi.org/10.11591/ijai.v13.i2.pp1398-1407
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