Authors - Aman Kumar, Kathan Nitin Patel, Aviral Sharma Abstract - Diabetes mellitus is perceived as a disease that significantly impacts a nation’s social, human, and financial expenditures. Concurrently, it is imperative to lower the prevalence rate and address the misunderstandings surrounding diabetes. An improved model that employs machine learning techniques to identify the behavior of diabetes in an individual. We have employed the parameters observed in the typical lifestyle, as well as the individual's emotional states and physical activities in the elderly age group. For a variety of test parameters, the proposed model implements a network classifier. It has been noted that this methodology yields effective results in the diagnosis of diabetes mellitus when the appropriate dataset is provided. The dataset utilized in this reseacrh study is the Indian PIMA dataset from the UCI Machine learning database. The detection of diabetes is contingent upon the presence of eight features in this dataset. The proposed Machine learning model has been implemented using a multilayer neural network that has been trained on backpropagation and feed-forward network simulation.
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