Abstract
Clinical advancements are one of the major out-comes of the technological phase shift of data sciences. The signif-icance of information technology in medical sciences by utilizing the Clinical Decision Support System (CDSS) has opened the spillways of exponentially improved predictive models. Utilizing the latest norms of classification algorithms on clinical data are widely incorporated for prognostic assessments. Medical experts have to make decisions that are crucial in nature and if the research can develop a mechanism that assists them in evolving solid reasoning, infer the knowledge and clearly express their clinical decision by justifying their assertions made, it will be a win-win situation. However, this field of science is still an unknown world for clinicians despite the fact that the enormous amount of medical data cannot be exploited to its maximum without invoking the technological support. The objective of this research is to introduce the clinicians and policymakers of the medical domain with the renowned computer-based methodolo-gies employed to construct a clinical decision support system. We expect that gaining the technical insight into the medical domain by the stakeholders will ensure commissioning the accurate and effective CDSS for improved healthcare delivery.
Highlights
Decision Support Systems (DSS) are the most studied areas of data sciences and their widespread adoption has earned standing in multiple domain such as education sector [48], customer relationship management [34], fraud detection in financial matters [33], detection of eavesdroppers and intruders in networks [39] and health care [6] including genetic programming [19]
The healthcare data classification mechanism assists medical experts in the early identification and management of medical malfunctioning and symptoms arisen in the patient
The major challenge of Clinical Decision Support Systems (CDSS) is to attain the utmost accuracy, which has the ability to provide a clinical decision process that generates an outcome with high precision value
Summary
Decision Support Systems (DSS) are the most studied areas of data sciences and their widespread adoption has earned standing in multiple domain such as education sector [48], customer relationship management [34], fraud detection in financial matters [33], detection of eavesdroppers and intruders in networks [39] and health care [6] including genetic programming [19]. Technological induction in health care has the strength to extract relationships within variables, identify the factors that may cause various risks and further impart fresh knowledge to yield befitting precision augmented with a convincing reasons [29]. This can only be achieved if the policymakers and clinicians have a deep insight into the technical strengths and weaknesses of computer-based methodologies being employed to construct CDSS. This data is either quantitative (numeric), qualitative (non-numeric), temporal data (based on timestamp) or time-series based
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