Diabetic Mellitus is one of the chronic diseases that affect many people around the globe. The severity of the disease and risk can be greatly reduced if it is predicted in the early stage. The main objective of the proposed model (T2DDP) is to predict type 2 diabetes mellitus and alert the patients well in advance to reduce the risk factor and severity associated with diabetes diseases. We have used supervised classification algorithms such as Naïve Bayes and ensemble algorithms like bagging with random forest and Adaboost for decision tree. The ensemble algorithm is mainly used to improve the performance by combining two or more models; this will aggregate the results of the entire model which can greatly enhance the accuracy and precision of predictions. Through this post, we are trying to build hybrid models that doctors can effectively use to treat diabetic patients. This method helps physicians to quickly group, identify and classify the disease type and manage it accordingly. Finally, the findings of the forecast will be submitted to the patient's cell phone at an early stage to make the immediate decisions about the health risk.
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