Issue |
BIO Web Conf.
Volume 152, 2025
International Conference on Health and Biological Science (ICHBS 2024)
|
|
---|---|---|
Article Number | 01020 | |
Number of page(s) | 12 | |
Section | Dense Matter | |
DOI | https://doi.org/10.1051/bioconf/202515201020 | |
Published online | 20 January 2025 |
Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors
1 Department of Information System, Universitas Harapan Bangsa, Karangklesem Purwokerto Selatan, 53144 Indonesia
2 Department of Informatics, Universitas Harapan Bangsa, Karangklesem Purwokerto Selatan, 53144 Indonesia
3 Department of Pharmacy, Universitas Harapan Bangsa, 53182 Kembaran Banyumas, Indonesia
4 Department of Information Technology, Universitas Harapan Bangsa, Karangklesem Purwokerto Selatan, 53144 Indonesia
* Corresponding author: rosyid@uhb.ac.id
Diabetes mellitus (DM) is a major health threat that can cause complications if early diagnosis and treatment are not carried out, 1.3 million children aged 6–18 or about 1.1% of the population of children in Indonesia are affected by this disease. Furthermore, the incidence of type 1 diabetes mellitus in children is on the rise in Indonesia but we do not have an accurate figure due to a high misdiagnosis rate. The aim of this study was to develop an artificial intelligence (AI)-based expert system for the early diagnosis of paediatric Type 1 DM using backward chaining and certainty factor methods. Backward Chaining is a reasoning method that starts with a hypothesis, then there is Certainty Factor method which is would make it become certainty by calculated the value from each symptom. Based on the National Diabetes Audit 2017-2021, the system processes clinical data such as HbA1c levels and symptoms. Testing shows accurate diagnoses about 79.2% for 10 validation tests with patients, aiding healthcare in underresourced areas. Future work includes expanding the dataset and integrating machine learning for improved adaptability.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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