Decision Tree-Based Use of Data Mining Techniques to Determine & Predict the Rare Patients Status The Case Study: Rare Patients Registry System (SABNA)

The health database contains a broad range of clinical data through which the discovered communication patterns and algorithms are led to new medical data achievements. Nowadays due to the existing integrated data as well as the informatics technology growth, it has turned into be a significant emergence. This study aims to identify the potential advantages with which data mining techniques can be applied for health and treatment trends using RADOIR’s patients' data as a case study. The most usual method is WEKA based data pre-processing, data mining and classification with decision tree to create prediction modalities via picturing a tree in order to analyze a rare disease. Result & Also we have used WEKA to evaluate the prediction processing via measuring and analyzing the sensitivity rate. As a result, there are some factors which a patient supporting center can take them into consideration while predicting the patients’ treatment costs and expenses.