Diabetes Diagnosis from Big Data using Fuzzy-Neural Chaotic Tree

Today, diabetes is a recognized global health concern. Global statistics show an increasing prevalence of the disease, posing a challenging issue for modern medicine. In response, computer science has proposed various methods to diagnose and predict diabetes. Nonetheless, researchers continue to work on resolving outstanding issues and errors. Data mining is used as a technical method for identifying and extracting new knowledge from data. This study introduces a novel approach for categorizing diabetic data that consists of three stages. Firstly, pre-processing is conducted, where data normalization procedures are applied. Subsequently, attribute extraction and selection are carried out. Finally, data mining principles are utilized for classification. The classification results obtained can be utilized to predict diabetes in various individuals. Evaluation of the results involves adherence to certain standards such as sensitivity, specificity, and accuracy. Our recommended approach, which combines chaotic fuzzy-neural with K-means tree, proves more effective than previous techniques, as confirmed by the results.

کلیدواژه ها:

Diabetes Detection ، Data mining ، fuzzy-neural ، Chaos Theory ، K-means Tree