Background: Provide a health care service to the patients with diabetes provides useful information that could be used to identify, treatment, following up and prevention of diabetes. Explore and investigation of large volumes of DATA requires effective and efficient methods for finding hiding patterns in the DATA. The use of various techniques of DATA MINING in particular Classification and Frequent patterns can be helpful.Methods: This article is a narrative review. We searched keywords related to application of DATA MINING in the field of diabetes, through related DATAbases, in English language articles published from 2005 to 2015. Also related articles in the selected articles list have been analyzed.Results: From the 2144 articles obtained in the initial search, 38 articles related to the subject of study, were selected. Several studies shown that classification and clustering algorithms, association rules and artificial intelligence are the most widely used DATA MINING techniques for predict the risk of diabetes has been successfully used.Conclusion: The important step in control of diabetes, use of the methods that could determine the possibility or lack of diabetes. According to studies conducted in this area seem to use DATA MINING techniques to prevent, treat and discover the connection between diabetes and its risk factors, can lead to significant improvements in the field of diabetes research and provide better health care for this group of patients.

Background: One of the today most common and incurable diseases that is associated with central neural system is ‘MS’ disease. Multiple sclerosis (MS) is a demyelinating disease in which the insulating covers of nerve cells in the brain and spinal cord are damaged. In this disease become apparent a wide spectrum of symptoms such as lose muscles control and their coordination and vision derangement. The goal of this research is to consider to two problems: 1- Recognition of effective CLINICAL symptoms on MS disease and 2- Considering levels of effectiveness of age, sex and education levels factors on MS disease and association between these factors according to verity of categories of this disease.Methods: DATA MINING science in medicine is worthy of attention with main application in diagnosis, therapy and prognosis, respectively high volume of collected datum. The DATA that were used in this article are about patients of Chaharmahal and Bakhtiari Province and collected by cure assistance. In this paper classification and association methods in software engineering field are used.Classification is a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood.Association rules are created by analyzing DATA for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships.Results: In consideration of first problem in this paper, concluded vision-CLINICAL symptoms are the most effective symptoms and in consideration of second problem, concluded that from 584 records, women affected four times more than men. In other word 70% of MS patients with high graduate are in relapsing-remitting category and 62.5% of MS patients are 20-40 years old.Conclusion: Some of symptoms are quite temporary and transitory and are ignored by people. Awareness of CLINICAL-symptoms prevalence manner can be warning for people before starting critical cycle of illness. This would cause early diagnosis, effective therapy and even prevention of disease progress, respectively to MS chronicity.

In this study, we sought to minimize the need for redundant blood tests in diagnosing common diseases by leveraging unsupervised DATA MINING techniques on a large-scale DATAset of over one million patients' blood test results. We excluded non-numeric and subjective DATA to ensure precision. To identify relationships between attributes, we applied a suite of unsupervised methods including preprocessing, clustering, and association rule MINING. Our approach uncovered correlations that enable healthcare professionals to detect potential acute diseases early, improving patient outcomes and reducing costs. The reliability of our extracted patterns also suggest that this approach can lead to significant time and cost savings while reducing the workload for laboratory personnel. Our study highlights the importance of big DATA analytics and unsupervised learning techniques in increasing efficiency in healthcare centers.