90 to 95% of cases of diabetes are of T2D. Both types are increasing rapidly, but the ratio of increase in T2D is higher than T1D.

Diabetes drastically spreads due to the patientвЂ™s inability to use the produced insulin. An absolute deficiency of insulin secretion causes type 1 diabetes (T1D). This disease is characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. Дiabetes is a metabolic disorder that impairs an individualвЂ™s body to process blood glucose, known as blood sugar. Diabetes mellitus is one of the extremely life-threatening diseases because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. However, for the last many years, there has been a considerable emergence of chronic and genetic diseases affecting public health. Governments are spending a considerable amount of their gross domestic product (GDP) for the welfare of the public, and initiatives such as vaccination have prolonged the life expectancy of people. Public health is a fundamental concern for protecting and preventing the community from health hazard diseases. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data.

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