Journal of Health & Medical Informatics

Diabetes Mellitus (DM) is the most common metabolic disorder. In developing countries, the prevalence of diabetes is increasing. It is important that every country should assess the magnitude of the problem and take steps to control and prevent DM and provide appropriate care. Despite all the advances in diabetes treatment, education remains the cornerstone of diabetes management. Diabetes education classes are useful for providing general information to be tailored to the specific needs of each patient. The web-based method seems to be effective in the continuing education. Therefore, the web-based method is recommended, as complementary to the face-to-face method, for designing and delivering some topics of continuing education programs. Web-based educational technologies allow diabetes educators to study how diabetes patients learn and which learning strategies are most effective. Since web- based educational systems are capable of collecting huge amounts of diabetic patient profile data, knowledge discovery techniques can be applied to find interesting relationships between attributes of diabetic patient, assessments, and the solution strategies adopted by the diabetic patients. This article presents an approach for predicting diabetic patient performance. The importance of this task lies in improving diabetic patient performance and the effective design of the online diabetes related courses.

Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. Fuzzy logic controller (FLC) was used to design a system for the diagnosis of eosinophilia. Eosinophilia is a common disease which is prevalent in people. High eosinophilic count in blood is an evidence for prevalence of eosinophilia. It is caused by both external and internal factors. The remedial measures for eosinophilia should be taken at the earliest, as the effect for it can vary from mild to severe. The design is based on Mamdani-style inference system which is very good for the representation of human reasoning and effective analysis. The implementation is done using MATLAB fuzzy logic tools. The effectiveness of FLC depends on the rules formed and interpretation of surface data. The performance of our FLC was predicted about 82.5% and a minimum error was obtained as 10.0%.