Enhanced Feed Forward Neural Network with Adam Optimization Model (Efnnao) For Predicting the Type 2 Diabetes Using Internet of Things

The Internet of Things (IoT) is a growing technology which connects things or objects with the internet and also enables things to collect and exchange data in the network. IoT plays a vital role in all domains, especially healthcare, where IoT used for monitoring patients and taking valuable decision for a particular problem. In the current era, diabetes is a common disease among most of the people. Diabetes is associated with many life-threatening diseases such as heart attack, kidney failure, Vison loss, Covid, etc., Type 2 diabetes is a type of diabetes that usually affects the elderly. Therefore, early detection or prediction can help prevent the patient from being at risk. However, accurately analyzing the dataset collected to make the right decision is one of the biggest tasks and improving the accuracy of the prediction model is another important task. There is several analysis models are available, over the years, various Neural Network models have been used in clinical diagnosis. However, these models are still sustained a particular level of error and less accuracy in training and testing of disease diagnosis. So, this paper proposed the Enhanced Feed forwarded Neural Network with Adam Optimization model (EFNNAO) including multiple layers of network that suitable for processing IoT based dataset. The proposed model effectively structured for predicting the type 2 diabetes in IoT environment. The designed network has the ability to learn every aspect of the dataset and perform calculations efficiently by avoiding under fitting and over-fitting. Finally, the proposed model is compared with other models which are in the same aspect. The proposed EFNNAO is outperformed than other models with 92.02% accuracy.