ABSTRACT
Advances in information technology and data collection methods have enabled high-speed collection and storage of huge amounts of data. Data mining can be used to derive laws fromlargedata volumes and their characteristics [1]. Similarly, fuzzy logic by facilitating the understanding of events is considered a suitable complement to scientific data mining. The present study used clustering to identify the independent characteristics of data. Related fuzzy sets, linguistic variables, and data classifications were defined, and the index was introduced based on the characteristics extracted from useful results. By considering the disease risk factors, the results were analyzed. Two factors contributing to the health improvement or deterioration were defined: 'age' and 'the appropriateness or inappropriateness between insulin level and blood sugar'. In addition, according to the results, fasting had a positive effect on fatty substances of the blood [cholesterol and triglycerides]. The results can help us determine whether or not an individual with a cardiovascular disease should fast in the month of Ramadan. However, due to variations in some features such as blood pressure throughout the day, there are uncertainties in some input data; therefore, the results could be far from reality. If it is possible to generate fuzzy data, then we can obtain more accurate results