Python scikit-fuzzy: developing a fuzzy expert system for diabetes diagnosis

Tajul Rosli Razak, Ahmad Zia Ul-Saufie, Mohamad Hanis Yusoff, Mohammad Hafiz Ismail, Shukor Sanim Mohd Fauzi, Nurul Ain Mohd Zaki


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.


Diabetes diagnosis; Fuzzy expert system; Medical application; Python programming

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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