Clinical Decision Support System

Abstract- Clinical Decision Support System (CDSS), with various data mining techniques being applied to assist physicians in diagnosing patient disease with similar symptoms, has received a great attention recently. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. In this paper, we have given the CDSS with some advance technologies like Support Vector Machine (SVM) classifier has offered many advantages over the traditional healthcare systems and opens a new way for clinicians to predict patient’s diseases. As healthcare is the field in which Security of data related to patient diseases are needs to be more secure, for that in this paper, we have use RSA

However, if no appropriate technique is developed to find great potential economic values from big healthcare data, these data might not only become meaningless but also requires a large amount of space to store and manage. Over the past two decades, the miraculous evolution of data mining technique has imposed a major impact on the revolution of human’s lifestyle by predicting behaviors and future trends on everything which can convert stored data into meaningful information. These techniques are well suitable for providing decision support in the healthcare setting. To speed up the diagnosis time and improve the diagnosis accuracy, a new system in healthcare industry should be workable to provide a much cheaper and faster way for diagnosis [1]. 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

Literature Survey

The authors, Ximeng Liu, Rongxing Lu, Jianfeng Ma in [1] proposed a privacy-preserving patient-centric clinical decision support system using naïve Bayesian classifier. By taking the advantage of emerging cloud computing technique, processing unit can use big medical dataset stored in cloud platform to train naïve Bayesian classifier. And then apply the classifier for disease diagnosis without compromising the privacy of data provider.

The authors, R. S. Ledley and L. B. Lusted [2] computer-assisted clinical decision support systems, who found that physicians have an imperfect knowledge of how they solve diagnostic problems. This article dealt with Bayesian and decision-analytic diagnostic systems and experimental proto- types appeared within a few years.

The authors [3] have performed some experiments for tumor detection in digital mammography. In this paper different data mining techniques, neural networks and association rule mining, have been used for anomaly detection and classification. From the experimental results it is clear that the two approaches performed well, obtaining a classification accuracy reaching over 70% percent for both techniques. The experiments conducted, demonstrate the use and effectiveness of association rule mining in image