Implementing Clinical Decision Support System Using Naïve Bayesian Classifier

Abstract

To speed up the diagnosis time and improve the diagnosis accuracy in today’s healthcare system, it is important to provide a much cheaper and faster way for diagnosis. This system is called as Clinical Decision Support System (CDSS). With various data mining techniques being applied to assist physicians in diagnosing patient diseases with similar symptoms, has received a great attention now a days. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. In this paper, the data mining technique name Naïve Bayesian Classifier, which offers many advantages over the traditional methods of data mining is used that opens a new way for clinicians to predict patient’s diseases. As the system is built on the sensitive data for patient privacy it is necessary to add some features that meets the security requirement. Specifically, with large amounts of data related to healthcare is generated every day, the classification can be utilized to excavate valuable information that improve clinical decision support system. Here the fuzzywuzzy string matching algorithm of naïve bayesian classifier is used to perform prediction from large number of symptoms data. The Result analysis perform in the last section on live data of five patient gives that by using proposed technique we try to make the Clinical Decision Support System more helpful for providing diagnosis of deceases more accurately and efficiently.