Abstract

This paper depicts the data mining technique for recognize most significant factors in barrenness couples to resolve the success rate of IVF (In-vitro Fertilization) treatment. The data set used in the research includes information outcome during IVF treatment and relevant laboratory tests. It supports the medical practitioner to identify tests have high impact factors for determining the success of infertility treatment. Data mining is associated with number of techniques that are used to perform Pre-processing, normalization and data reduction. The reduced data set includes the major set of characters which are impacts the outcome that can be used to infer and predict. The initial data set are pre-processed by the supervised filter and the three different attribute selection techniques before prediction. It is significant to precisely analyze the data set and clean the superfluous data that maximize the prediction accuracy. Reduced data is then given to the Feed Forward Neural Network based classification algorithm such as Multilayer Perceptron algorithm. This algorithm performs better as compare to other algorithms