| 总结: | Diagnosis of diabetes is a complex decision-making process. The creation of diabetes diagnosis models is vital in the decision-making process and requires adequate information for fast detection and treatment. Diabetes is detected from a set of symptoms. The symptoms data are an important reference to diagnose diabetes which are collected and stored in datasets. Diabetes datasets are prone to vagueness and uncertainty. In addition, insufficient information on the diagnosis of diabetes exists and this problem is not addressed in previous research. This research work analyzes a simulated diabetes treatments dataset that were validated by medical expert [1]. A new fuzzy inference model based on Mamdani method is designed to provide interpretable understanding and sufficient information on diabetes diagnosis which is combied with the level of care to support the vagueness, uncertainty, and insufficient information problems. © 2023 Little Lion Scientific.
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