Regression Based Fuzzy Logic Classification Approach for Non Linear Data Set in Health Care System

Abstract

Data processing, popularly known as data discovery in giant information, allows companies and organizations to form calculated choices by grouping, accumulating, analyzing and accessing company information. Authors propose a sturdy metaphysics based mostly thirddimensional 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, during which map and information views representational process similarity and comparison of attributes extracted from warehouses, square measure utilized in this studies, for understanding the ailments supported gender, age, geography, food habits and hereditary traits. Statistic statement takes the past values of a statistic and uses them to forecast the longer term values. Fuzzy regression strategies have normally been wont to develop shopper 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 normally won"t to model client preferences. We tend to propose a Testing the strength of Exponential Regression Model over regression toward the mean Model.