Chronic kidney disease diagnosis using Fuzzy Knowledge Graph Pairs-based inference in the extreme case
Pham Minh Chuan, Luong Thi Hong Lan, Tran Manh Tuan, Nguyen Hong Tan, Cu Kim Long, Pham Van Hai, Le Hoang Son
Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 83–88 (2022)
Abstract. Chronic kidney disease is one of the diseases with high morbidity and mortality, commonly occurring in the general adult population, especially in people with diabetes and hypertension. Scientists have researched and developed intelligent medical systems to diagnose chronic kidney disease. Nevertheless, healthcare services remain low in resource-limited areas, and general practitioners are very short of clinical experience. Identifying chronic kidney disease in clinical practice remains challenging, especially for the general practitioner. This study proposes a model to develop a model for improving the efficiency of differential diagnosis. This paper presents a model consisting of a fuzzy knowledge graph pairs-based inference mechanism by accumulating the new rules to enrich the fuzzy rule base. A real-world dataset is gathered in Dien Bien hospital to evaluate the performance of our proposed model.
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