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Computational decision support system in healthcare: a review and analysis

Journal: International Journal of Advanced Technology and Engineering Exploration (IJATEE) (Vol.8, No. 75)

Publication Date:

Authors : ;

Page : 199-220

Keywords : DSS; ICT; Data mining; Machine learning; Soft computing; Evolutionary algorithms.;

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A decision support system (DSS) may help to synchronize comprehensive computational aspects of computational problems like knowledge discovery, processing, and pattern visualization. It provides an effective channel for decision making by churning huge datasets. Primarily, DSS has been deployed in domains like business process management, health informatics, and even for managing smart devices. Numerous algorithms have been proposed to augment the efficacy of DSS and are undergoing refinement. Most recently, DSS has been trialed in the healthcare industry because there is a need for an intelligent decision support system. It may be helpful in the process of automation along with the solution generation and evaluation. In this paper, broad analysis and discussion on the applicability and suitability of the methods related to DSS and algorithms have been discussed along with the role of information and communications technology (ICT). It also includes problem-based discussion and suggested solutions along with different cases in the healthcare system. It covers the data analysis with the domain intelligence impact along with the information processing for knowledge extraction and discovery. It also covers some of the clinical decision aspects for understanding the impact of other correlated medical resource systems. The methodological and computational analysis includes data preprocessing, knowledge extraction, interpretation, decision-making model, and the influencing factors in the performance analysis. The main data mining methods considered here for the DSS system in case of healthcare informatics discussion were association rule mining, clustering, classification and optimization algorithms. The machine learning aspects covered here in three ways supervised, semi-supervised, and unsupervised for the decision analysis based on the healthcare system. Finally, based on the methodological and computational applicability different decision-making scenarios have been discussed and analyzed for the analysis of the combination and nature of applicability. Our study and analysis provide an analytical and computational perspective in terms of the health care system, influencing parameters, their applicability, a methodological perspective, decision-making process, traditional methods, and the challenges along with the suggested measures for the future. It's also helpful in the process of maintaining the internal and external aspects which is more reliable in performance aspects of DSS.

Last modified: 2021-03-06 15:06:04