IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 04 Issue: 03 | Mar-2015, Available @ http://www.ijret.org 519 CORRELATION OF ARTIFICIAL NEURAL NETWORK CLASSIFICATION AND NFRS ATTRIBUTE FILTERING ALGORITHM FOR PCOS DATA K. Meena 1 , M. Manimekalai 2 , S. Rethinavalli 3 1 Former Vice-chancellor, Bharathidasan University, Tricy, Tamilnadu, India 2 Director and Head, Department of Computer Applications, Shrimati Indira Gandhi College, Trichy, TN, India 3 Assistant Professor, Department of Computer Applications, Shrimati Indira Gandhi College, Trichy, TN, India Abstract Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately. Keywords: ANN, SVM, PCA, CFS --------------------------------------------------------------------***---------------------------------------------------------------------- 1. INTRODUCTION The polycystic ovary syndrome (PCOS) in women is pretended by the endocrinological which is the most common issue. The following changes like hirsuitism acne, oligo or amenorrhoea, anovulation, morphological change and showing the increased levels of serum androgen and these are demonstrated with the PCOS women on the evidence of the ovary on the ultrasonography. Diagnostically, the above is the current practice and it is the agreed criteria in Rotterdam 2003 [1]. About 50% of the general population, the patients with PCOS are obese which are the higher prevalence. Due to the condition of the metabolic element, the result of the long-term morbidity by the insulin resistance. The small cysts and the clusters of pearl-sized which induces the PCOS frequently because it is referred as the polycystic when there are many number of cysts in the ovaries. The fluid-filled form of the immature eggs are contained by the cystic. The symptoms of the PCOS are contributed due to the large number of male hormones productions by the Androgen [2]. The programming of utero fetal is brings out by the PCOS phenotype which is the plays the major role in the adult age of women and it might be the cause for the PCOS. Due to the interaction of the genetic factors with the obesity, the metabolic characteristic and result of the menstrual disturbances are the last expressions of the phenotype of PCOS. [3] (factors of environment) which leads the women for developing of PCOS and it is genetically inclined. Nowadays, data mining is the exploration of large datasets to extort hidden and formerly unknown patterns, relationships and knowledge that are complicated to detect with conventional statistical methods. In the emerging field of healthcare data mining plays a major role to extract the details for the deeper understanding of the medical data in the providing of prognosis [4]. Due to the development of modern technology, data mining applications in healthcare consist about the analysis of health care centres for enhancement of health policy-making and prevention of hospital errors, early detection, prevention of diseases and preventable hospital deaths, more value for money and cost savings, and detection of fraudulent insurance claims. The characteristic selection has been an energetic and productive in the field of research area through pattern recognition, machine learning, statistics and data mining communities. The main intention of attribute selection is to