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Application of Artificial Neural Networks for classification and prediction

Robot holds a human brain in his hand. 3D illustration. Contains clipping path

Developing tools for disease classification can be extremely extensive and challenging task, especially when the association between input and target values is non-linear and depending on multiple factors. Machine learning methods such as Artificial Neural Networks (ANNs) have been considered as promising tools for overcoming these difficulties since they do not require analytical model of observed process. The theory of neural networks is still growing field due to their ability to derive meaning from complicated or imprecise data and because they use different approach, parallel data processing instead of algorithmic approach to problem solving like conventional computers.

Different ANN architectures have been used for various purposes, such as classification, pattern recognition, prediction, control and optimization. The neural networks, in terms of data processing, mimic physical structure of human nervous system consisting of artificial neurons. Once trained, ANN is able to predict unknown future outcomes of the same process. ANNs can be classified into two groups based on internal information flow: feedforward and feedback neural networks.

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