An iPhone-based application for promoting type 2 diabetic patients self- management towards healthy lifestyle habits

— In 2000, according to the World Health Organization, at least 171 million people, 2.8% of the population worldwide, suffered from diabetes. The Centres for Disease Control has defined it as an epidemic disease. Its incidence is increasing rapidly, and it is estimated that by 2030 this number will almost double. Diabetes mellitus occurs throughout the world, but is more common (especially type 2) in the more developed countries. Diabetes is a chronic condition that occurs when pancreas does not assure enough insulin secretion or when the body does not consume the insulin produced. Insulin is a hormone that regulates blood sugar. The effect of uncontrolled diabetes is the hyperglycaemia (blood sugar), which eventually seriously damage many organs and systems, especially the nerves and blood vessels. Diabetes type 2 (most common type of diabetes) is highly correlated with elderly people, obesity or overweight. Promoting a healthy lifestyle helps patients to improve their quality of life and in many cases to avoid complications related to the disease. This paper is intended to describe an iPhone-based application for self-management of type 2 diabetic patients, which allow them improving their lifestyle through healthy diet, physical activity and education

Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.

About t-norms on type-2 fuzzy sets.

Walker et al. defined two families of binary operations on M (set of functions of [0,1] in [0,1]), and they determined that, under certain conditions, those operations are t-norms (triangular norm) or t-conorms on L (all the normal and convex functions of M). We define binary operations on M, more general than those given by Walker et al., and we study many properties of these general operations that allow us to deduce new t-norms and t-conorms on both L, and M.