The decision support system in health care industry is used to contribute to the environment from disease diagnosis to treat the illness. In the initial stage, the test process is being used in the disease diagnose with more economical rate of the price. The ultimate purpose of the dataset process is to get know about the disease to start the treatment. In this process, there is need for a decision support system that could be used in rational decision making to improve the techniques of artificial intelligence, cognitive science, and machine language. The computer system is used as tool to deal with that problem to increase the efficiency of the test system, and these systems are known as Decision Support System (G. Alexouda, 2005).

The motivation in the objective of Decision Support System is used in health care center to give an overview of the health structure in the country. Decision Support System helps overcome the deficiencies in cognitive science which are collaborated with integrity in different industries. The data existing bin the Decision Support System is used in dataset in healthcare industry. There is considerable amount of decision making in the health issues to resolve them. The system is immediately becoming an important tool for providers of healthcare as the quantity of the available information that maximizes responsibilities for delivering value-based care. The system is important in the reduction of the variations in the clinic related methods or procedures and the duplicative test, avoiding the complications as well as making sure the safety of the patients that may have some outcomes as the readmission in the expensive hospitals are the top priorities for the environment reimbursement as well as the providers within the modern regulatory environment as well as joining of the big data insights which are hidden and essential to attain the goals. Decision making support system is very important in every department of the industry to make the rational decision in the production process. (DSS) It could be used combining different resources to get the maximum satisfaction from the production within the given figures in test used in the test system. Decision Support System is also used in the artificial intelligence of the computer system to make a rational decision. Good decision making use improves the efficiency of the diagnose system and treatment of the disease. The management of any industry, when they want to improve their performance and decision making, prefer to use decision support system to evaluate the decision-making skills in the organization.

As concerned with the decision support system in the health care industry, there are a lot of examples that relate to that in industry, and the medical industry is being used different policies to intervene in the diagnose and treatment of the disease with great efficiency. Machine language and artificial intelligence are being used in modern machines, which are used by the medical experts to recognize the health issues, and it is also set out in the further goals of the health industry to bring new and innovative methodology in testing the disease.

References of decision support systems in this industry can capitalize on the new technologies such as machine learning, artificial intelligence, big data, and the internet of things, etc. to improve the decision-making process.

E. Aktaş, Ülengin, F. & Şahin, Ş. Ö., 2007. A decision support system to improve the efficiency of resource allocation in healthcare management.. Socio-Economic Planning Sciences, 41(2), pp. 130-146..

G. Alexouda, 2005. A user-friendly marketing decision support system for the product line design using evolutionary algorithms.. Decision support systems, 38(4), pp. 495-509.

K. B. Matthews, Sibbald, A. R. & Craw, S., 1999. Implementation of a spatial decision support system for rural land use planning: integrating geographic information system and environmental models with search and optimisation algorithms.. Computers and electronics in agriculture, 23(1), pp. 9-26..

R. Bose, 2003. Knowledge management-enabled health care management systems: capabilities, infrastructure, and decision-support.. Expert systems with Applications, 24(1), pp. 59-71..