An Intelligent Decision Support System Model for Health Care Planners using Fuzzy Logic

Abstract

The main objective of this research work is to evolve fuzzy logic through improvement Intelligent Decision Support System Model for Health Care Planners so that novel & hidden knowledge patterns can be generated to automate & quicken the process of decision making in clinical diagnosis as well as other domains of health care management. In the healthcare sector quality demands are rising for designing expert systems for medical diagnosis. At the same time growing capture of biological, clinical, administrative data and integration of distributed and heterogeneous databases create a completely new base for medical quality and cost management. Against this background we applied intelligent data mining methods for analyzing medical repositories. In order to reach the main goal of the research, applications of fuzzy logic are to be explored on medical databases to discover knowledge. we have used databases namely health disease, Immunization Program (Polio Database), and Medical Dataset of Patients collected from free Internet repository, Public health care sector and from renowned private nursing homes. This implementation uses innovatively designed fuzzy logic rules so that it can be queried and then consulted in a proper, defined way helping in beneficial future analysis. This campaign can classified immunization programmers into Efficient, Satisfactory and Poor categories based on the number of children immunized and number of houses left subsequently with the observation for improvement. Analysis of this type of classification facilitates identification of areas needing attention and more resources to improve performance. These experiments can work synergistically which can produce single knowledge pattern or different pieces of knowledge patterns so that health care planners can take advantage of this knowledge discovery to lower healthcare costs while improving healthcare quality. The results of the experiment shows that fuzzy logic integrated knowledge discovery on immunization data helps decision makers to improve the efficiency of Immunization Programmers in Indian States by proper monitoring and categorization of the health centers, supervisors of health schemes, and their performances. These results can simulate the answer of the research question. Can we provide optimized answers to a medical problem which is imprecise, partial truth and uncertain.