DIABETES DIAGNOSIS USING MACHINE LEARNING

Artificial neural networks have been in the position of producing complex dynamics in control applications over the last decade, especially when they are linked to feedback. Although ANNs are strong for network design, the harder the design of the network, the more complex the desired dynamic is. Many researchers tried to automate the design process of ANN using computer programs. Search and optimization problems can be considered as the problem of finding the best parameter set for a network to solve a problem. Recently, the problem of optimizing ANN parameters to train different research datasets has been targeted by two commonly used stochastic genetic algorithms (GA) and particle swarm optimization (PSO). The process based on the neural network is optimized with GA and PSO to enable the robot to perform complex tasks. However, using such optimization algorithms to optimize the ANN training process cannot always be balanced or successful. These algorithms simultaneously aim to develop three main components of an ANN: synaptic weight, connections, architecture and transfer functions set for each neuron. Developed with the proposed approach, ANN is also compared with hand-designed Levenberg-Marquardt and Back Propagation algorithms.