Published Paper Details:

A neural network is a collection of algorithms that aims to identify underlying links in a set of data using a method that imitates how the human brain functions. Numerous people suffer from diabetes mellitus, one of the most serious diseases. Age, obesity, inactivity, genetic diabetes, a poor diet, high blood pressure, and other factors can all contribute to diabetes mellitus. The healthcare sector greatly benefits from big data analytics. Numerous ML algorithms are employed to forecast the occurrence of diabetes. One machine learning (ML) model used to foretell a patient's likelihood of having diabetes is the neural network. Neural networks are made up of a large number of artificial neurons, each of which has an activation function. An artificial neuron's activation function is the part of the neuron that produces an output in response to inputs. Activation functions are crucial because they aid in understanding and learning about complex, non-linear mappings between inputs and outputs. The most popular activation functions and a special activation function with higher accuracy are discussed in this study for the neural network that predicts diabetes.