On Fuzzy Mathematical Modeling in the Analysis of Healthcare for Optimizing Human Productivity Loss

Abstract

Preventive health care has always been the preferred option for creating newlineawareness and reducing diseases in public. The importance and significance of planning newlinein healthcare can hardly be over emphasized today when providing proper and adequate newlineservice continues to be a key concern of most countries. For growing longevity and newlineageing population amidst dwindling birth rates, many countries are increasingly hardpressed newlinefor the extra budget and resources to meet the healthcare needs. Some of the key newlinehealthcare issues considered in Mathematical Modeling today include estimation of newlinefuture demand for services in order to build enough capacity, storage of medicines for newlinecovering a target population and design of the emergency facilities for efficient handling newlineof patients. newlineMedicine and health care is a fertile environment for vague, conflicting, and not newlinedefinitive decisions, and therefore diagnosis, assessment, controlling, and therapeutic newlineconduct. When dealing with this kind of information, that is simultaneously uncertain newlineand imprecise, then the decision is considered to be under approximation. In order to newlinedeal with this kind of problem, a general theory of approximate reasoning was proposed newlineby Zadeh [71]. This reasoning methodology addresses the interface between numbers newlineand symbols by using fuzzy set theory approach. newlineThe theory of fuzzy sets introduced by Zadeh in 1965[71], has gained recognition newlineas a mathematical tool by which imprecise or inexact or subjective data can be dealt with newlinein a very logical manner. Fuzzy logic is an inference morphology that enables human newlinereasoning and is applied to knowledge based systems. Fuzzy logic helps to capture newlineuncertainty for computing and reasoning. Currently, the use of fuzzy set and logic in the newlinefield of medicine area diagnosis, treatment of illnesses, and patient pursuit has highly newlineincreased. This computational logic uses truth degrees as a mathematical model of the newlinevagueness phenomenon while probability is a mathematical model of ignorance.