Abstract:Introduction: Bread is a food full of flavors and nutrients and continues to be the basis of our daily diet. Aim: The aim of this review was the investigation of bread's usefulness in our daily dietary chain and health in general. Material-Method: Extensive review of the recent literature was conducted in electronic databases (Medline, Scopus, and Google Scholar) through the Association of Hellenic Academic Libraries (HEAL-Link) using the appropriate key words: bread, health, disease as well as a combination of them. Results: Bread is an excellent source of complex carbohydrates that provide the body with the energy needed. Although bread is a basic food of everyday human nutrition, it is implicated in a variety of situations that burden the human body, such as obesity. However, this happens only when it is consumed in very large quantities and when it is part of an unbalanced diet. Conclusions: Bread, when is consumed in balanced quantities can give the body plenty of nutrients essential for its good function.

This study proposes an Artificial Neural Network (ANN) and Genetic Algorithm model for diagnostic risk factors selection in medicine. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Networks (PNNs) were used to face a medical disease prediction. Genetic Algorithm (GA) was used for pruning the PNN. The implemented GA searched for optimal subset of factors that fed the PNN to minimize the number of neurons in the ANN input layer and the Mean Square Error (MSE) of the trained ANN at the testing phase. Moreover, the available data was processed with Receiver Operating Characteristic (ROC) analysis to assess the contribution of each factor to medical diagnosis prediction. The obtained results of the proposed model are in accordance with the ROC analysis, so a number of diagnostic factors in patient's record can be omitted, without any loss in clinical assessment validity.

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