Survey: Techniques Of Data Mining For Clinical Decision Support System

Abstract

Clinical decision support system, which uses advanced data mining techniques access as well store data on server. The advantages of clinical decision support system include not only providing diagno sis accuracy but also minimize diagnosis time .Typically, with large amounts of clinical data generated every day, naive Bayesian classification can be utilized to formed valuable information to improve clinical decision support system. The Clinical Decisi on support system is very flourishing but it also having some critical problems. I propose a new privacy -preserving patient-centric clinical decision support system, which helps clinician complementary to diagnose the risk of patients’ disease in a privacy-preserving way. Also , the past patients’ historical data are stored in cloud and can be used to train the naive Bayesian classifier without leaking any individual patient medical data, and then the trained classifier can be applied to determine the disease risk for new coming patients and top-k disease names are also extracted from their according to the own preferences, which is provided for protecting the privacy of past patients’ historical data, a new cryptographic tool called additive homomorphism proxy aggregation scheme is designed. Moreover, to leverage the leakage of na¨ıve Bayesian classifier, we introduce a privacy -preserving top-k disease names retrieval protocol in our system. The privacy analysis gives security t the patient information and will not be leaked out at the time of disease diagnosis phase. This can be concluding that our system can efficiently calculate patient’s disease risk with high accuracy in a privacy-preserving way.