Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors
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 melli...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2025/03/bioconf_ichbs2025_01020.pdf |
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| author | Al-Hakim Rosyid R. Setiawan Retno Agus Hidayat Rachman Arkananta Edgina R. Samodra Galih Jayusman Hadi Suryani Riska Famuji Tri Styo |
| author_facet | Al-Hakim Rosyid R. Setiawan Retno Agus Hidayat Rachman Arkananta Edgina R. Samodra Galih Jayusman Hadi Suryani Riska Famuji Tri Styo |
| author_sort | Al-Hakim Rosyid R. |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aac64f7841cb4d29abc71c7674d507d3 |
| institution | Kabale University |
| issn | 2117-4458 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj-art-aac64f7841cb4d29abc71c7674d507d32025-02-05T10:42:50ZengEDP SciencesBIO Web of Conferences2117-44582025-01-011520102010.1051/bioconf/202515201020bioconf_ichbs2025_01020Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factorsAl-Hakim Rosyid R.0Setiawan Retno Agus1Hidayat Rachman2Arkananta Edgina R.3Samodra Galih4Jayusman Hadi5Suryani Riska6Famuji Tri Styo7Department of Information System, Universitas Harapan Bangsa, Karangklesem Purwokerto SelatanDepartment of Information System, Universitas Harapan Bangsa, Karangklesem Purwokerto SelatanDepartment of Informatics, Universitas Harapan Bangsa, Karangklesem Purwokerto SelatanDepartment of Information System, Universitas Harapan Bangsa, Karangklesem Purwokerto SelatanDepartment of Pharmacy, Universitas Harapan BangsaDepartment of Information System, Universitas Harapan Bangsa, Karangklesem Purwokerto SelatanDepartment of Information System, Universitas Harapan Bangsa, Karangklesem Purwokerto SelatanDepartment of Information Technology, Universitas Harapan Bangsa, Karangklesem Purwokerto SelatanDiabetes 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.https://www.bio-conferences.org/articles/bioconf/pdf/2025/03/bioconf_ichbs2025_01020.pdf |
| spellingShingle | Al-Hakim Rosyid R. Setiawan Retno Agus Hidayat Rachman Arkananta Edgina R. Samodra Galih Jayusman Hadi Suryani Riska Famuji Tri Styo Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors BIO Web of Conferences |
| title | Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors |
| title_full | Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors |
| title_fullStr | Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors |
| title_full_unstemmed | Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors |
| title_short | Rule-based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors |
| title_sort | rule based ai system for early paediatric diabetes diagnosis using backward chaining and certainty factors |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2025/03/bioconf_ichbs2025_01020.pdf |
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