hedieh zabolinezhad

World Health Organization (WHO) estimates that the number of people with diabetes will grow 114% by 2030. It declares that patients themselves have more responsibility for controlling and the treatment of diabetes by being provided with updated knowledge about the disease and different aspects of available treatments, and diet therapy in particular. In this regard, diet recommendation systems would be helpful. They are techniques and tools which suggest the best diets according to patient’s health situation and preferences. Accordingly, this narrative review studied food recommendation systems and their features by focusing on nutrition and diabetic issues. Literature searches in Google scholar and Pubmed were conducted in February 2015. Records were limited to papers in English language; however, no limitations were applied for the published date. We recognized three common methods for food recommender system: collaborative filtering recommender system (CFRS), knowledge based recommender system (KBRS) and context-aware recommender system (CARS). Also wellness recommender systems are a subfield of food recommender systems, which help users to find and adapt suitable personalized wellness treatments based on their individual needs. Food recommender systems often used artificial intelligence and semantic web techniques. Some used the combination of both techniques.

Timely diagnosis of liver cirrhosis is vital for preventing further liver damage and giving the patient the chance of transplantation. Although biopsy of the liver is the gold standard for cirrhosis assessment, it has some risks and limitations and this has led to the development of new noninvasive methods to determine the stage and prognosis of the patients. We aimed to design an artificial neural network (ANN) model to diagnose cirrhosis patients with non-alcoholic fatty liver disease (NAFLD) using routine laboratory data. Data were collected from 392 patients with NAFLD by the Middle East Research Center in Tehran. Demographic variables, history of diabetes, INR, complete blood count, albumin, ALT, AST and other routine laboratory tests, examinations and medical history were gathered. Relevant variables were selected by means of feature extraction algorithm (Knime software) and were accredited by the experts. A neural network was developed using the MATLAB software. The best obtained model was developed with two layers, eight neurons and TANSIG and PURLIN functions for layer one and output layer, respectively. The sensitivity and specificity of the model were 86.6% and 92.7%, respectively. The results of this study revealed that the neural network modeling may be able to provide a simple, noninvasive and accurate method for diagnosing cirrhosis only based on routine laboratory data.