Fuzzy inference systems for predicting and evaluating diabetes likelihood and severity
Author(s): Bed Prakash Singh and Brijendra Kumar
Abstract: Since the prevalence of diabetes continues to increase across the globe, there exists an increased demand of accurate and convenient methods of predicting and determining the severity of this chronic illness. This analysis explores why Fuzzy inference system (FIS) is such an informative and flexible tool that can be used to predict and determine the seriousness of diabetes. The standards of fuzzy rationale are employed by FIS to prove the innate susceptibility and laxity of clinical data pertaining to diabetes. The research starts with the establishment of a complete dataset comprising of the relevant clinical variables such as the level of blood glucose, age and body mass index (BMI). Then a predictive model is developed using FIS which considers the complex relationships between these variables. The functioning of the membership and fuzzy rules have been well structured to replicate the knowledge of a professional medical worker in finding out the chances of having diabetes and the severity. The suggested FIS model is comprehensive in its approval using verifiable clinical data on a variety of patient groups. The analysis of the displayed model is conducted on the bases of awareness, specificity, and, to a greater extent, accuracy in the prediction of the seriousness of diabetes. The shortcomings and advantages of the FIS approach are also characterized by near investigations using existing analytic strategies. FIS model is also expanded to analyze the efficiency of different treatment choices to decrease the severity of diabetes. The FIS demonstrates its real potential as a choice help device when it comes to making medical care specialists fit customized therapy plans by consolidating the dynamic sources of data and changing according to the changing circumstances of the patients. The post-exploration consequences contribute to the emerging collection of writing on shrewd frameworks of diabetes the board. FIS is an encouraging instrument of forecasting and assessing the level of diabetes because of its flexibility and deciphering, which will lead to a more accurate and tailored treatment in the global struggle against the health problem.
Bed Prakash Singh, Brijendra Kumar. Fuzzy inference systems for predicting and evaluating diabetes likelihood and severity. Int J Stat Appl Math 2025;10(12):29-40. DOI: 10.22271/maths.2025.v10.i12a.2206
