Fuzzy Logic Classification Approach for Prediction of Patient Data in Health Care System using Data Mining Analysis

Abstract

Data Mining is the process of extracting data from huge information sets through various techniques drawn from the sector of Statistics, Machine Learning and information base Management Systems. Data processing, popularly known as data discovery in huge information, is carrying out of operations on data to retrieve, transform, or classify information. Authors propose a study of third-dimensional information deposit and mining approach to deal with the problems of organizing, reportage and documenting polygenic disease cases as well as causalities. Data processing procedures views representational process similarity and comparison of attributes extracted from the information gathered. Statistic statement takes the past values of a statistic and uses them to forecast the longer term values. Fuzzy regression strategies have been used to develop preferences models that correlate the engineering characteristics with shopper preferences relating to a replacement product; the patron preference models offer a platform, wherever by product developers will decide the engineering characteristics so as to satisfy shopper preferences before developing the merchandise. Recent analysis shows that these fuzzy regression strategies area units are normally used to model client preferences. We tend to propose a Testing the strength of Exponential Regression Model over regression Model to predict the patient data.