BACKPROPAGATION LEARNING ALGORITHM BASED ON LEVENBERG MARQUARDT ALGORITHM

Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily compressible to humans. Data Mining represents a process developed to examine large amounts of data routinely collected. The term also refers to a collection of tools used to perform the process. One of the useful applications in the field of medicine is the incurable chronic disease diabetes. Data Mining algorithm is used for testing the accuracy in predicting diabetic status. Fuzzy Systems are been used for solving a wide range of problems in different application domain and Genetic Algorithm for designing. Fuzzy systems allows in introducing the learning and adaptation capabilities. Neural Networks are efficiently used for learning membership functions. Diabetes occurs throughout the world, but Type 2 is more common in the most developed countries. The greater increase in prevalence is however expected in Asia and Africa where most patients will likely be found by 2030. This paper is proposed on the Levenberg – Marquardt algorithm which is specifically designed to minimize sum-of-square error functions. Levernberg-Marquardt algorithm gives the best performance in the prediction of diabetes compared to any other backpropogation algorithm.