Abstract
Abstract: Diabetes mellitus is a scientific ailment defined by hyperglycemia caused by a lack of absolute or relative insulin efficiency in the human body. Diabetes prediction is one of the newest and fastest-growing technologies in medical data analysis. The clustering method for grouping diabetes data based on cluster head properties is the focus of this study. This study proposes a new BG prediction method called RNN, which is based on recurrent neural networks (RNN). The probability of values of the variable is calculated using the probability density function (PDF). For pre-processing and missing value analysis, we used an enhanced Decision Tree and a weighted K-means method. The proposed method uses the weighted Binary Bat Optimization algorithm for feature selection. In terms of diabetic categorization, numerical findings reveal that when compared to other existing methods, the DP-RNN approach with PDF produces the way PDF-DPRNN produces the more accurate classification result. Keywords: PDF, RNN, Decision Tree, Bat Optimization, Diabetes mellitus
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