چكيده به لاتين
The scope of the study is the use of data mining techniques in the medicine specifically the health technologies assessment. When using medical data mining in the evaluation topics, issues such as patient privacy and unusual storage of information are fully taken into account and their shortcomings are identified and resolved. Purpose: The purpose of the thesis is to investigate and develop the application of medical data mining to improve the efficiency of information analysis in the field of medicin, moreover, predicting cancer with minimum of Para clinical tests in medical imaging and health care management. Research method: To achieve this goal, the following activities were carried out: initially, the analysis of existing process models; second, design and evaluation of medical models; after that, development of screening primitive information for diagnosis and prediction. This research is based on changes in the Crisp Model (Industrial Data Mining Model) with regard to the special considerations of the field of medicine called Crisp Med DM. This model has been successfully used to predict breast cancer screening by using predictive models and results of the method, it can be used to treat patients early in the early stages more rapidly and to create costly, financially and What psychiatric conditions prevent breast cancer among their patients and their families. Conclusion: In this research, three commonly used data mining algorithms are used in the field of medicine. Moreover,decisions tree with accuracy of 78%, Association Rule(GRI algorithm )with accuracy of 100% ; furthermore, Clustering and created 4 optimal clusters. These algorithms bread some models which use a minimum of clinical and Para clinical parameters in medical imaging , a variety of carcinomas have been identified before the malignancy has been sampled.
