Rosyid R. Al-Hakim, Retno Agus Setiawan, Rachman Hidayat, Edgina R. Arkananta, Galih Samodra, Hadi Jayusman, Riska Suryani, Tri Styo Famuji
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 under-resourced areas. Future work includes expanding the dataset and integrating machine learning for improved adaptability. © The Authors, published by EDP Sciences.
Department of Information System, Universitas Harapan Bangsa, Karangklesem Selatan, Purwokerto, 53144, Indonesia; Department of Informatics, Universitas Harapan Bangsa, Karangklesem Selatan, Purwokerto, 53144, Indonesia; Department of Pharmacy, Universitas Harapan Bangsa, Kembaran, Banyumas, 53182, Indonesia; Department of Information Technology, Universitas Harapan Bangsa, Karangklesem Selatan, Purwokerto, 53144, Indonesia
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