Clinical Decision Support System in Computational Methods: a Review Study

Abstract

Clinical Decision Support Systems (CDSS) are computational models designed impact clinical decision making about individual patients at the point in time that these decision are made. Clinical Decision Support Systems (CDSS) form an important area of research. While traditional systematic literature surveys focus on analyzing literature using arbitrary results, visual surveys allow for the analysis of domains by using complex network-based analytical models. In this paper, we present a detailed visual survey of CDSS literature using important papers selected. The aim of this study is to review a number of articles related to CDSS for heart and stroke diseases. In this study several articles are comparable to the computational methods and rules used for data processing. From the analysis of several sources of literature, the computational methods and rules used in CDSS are Principle Component Analysis (PCA), Support Vector Machine (SVM),  Naïve Bayes data mining algorithm, Case Based  Recommendation Algorithm, Weighted Fuzzy Rules, Ontology Reasoning, TOPSIS Analysis, Genetic Algorithms, Fuzzy Neural network, Case-based reasoning (CBR), Weighted Fuzzy Rules and Decision Tree.