Using the most advanced technologies in programming neural networks and computer training based on past periods
- Neural Network Model for Flu Sickness Rate prediction.
- We propose new quantitative methods utilizing deep learning for flu sickness rate forecast.
- The accuracy of predictions according to test results is 85%.
- The neural network consists of a group of receptors, 4 hidden layers and one output neuron.
- The group of receptors is supplied with a segment of historical flu sickness data.
- Each hidden layer of a neural network consists of n-neurons; each following layer is completely connected to the next one.
- The function of activating hidden blocks ensures non-linearity of the network.
- The network was trained by using the Back Propagation (BP) algorithm.
- The output layer consists of one output neuron which is producing an appropriate sickness rate.
- The node of the output level has a sigmoidal activation function.