An Efficient Prediction Model for Diabetic Database using Soft Computing Techniques Veena H Bhat 1 , Prasanth G Rao 1 , P Deepa Shenoy 1 , Venugopal K R 1 , and L M Patnaik 2 1 University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India. 2 Vice Chancellor, Defence Institute of Advanced Technology, Deemed University, Pune, India. veena.h.bhat@gmail.com; prasanthgrao@gmail.com; shenoypd@yahoo.com Abstract. Organizations aim at harnessing predictive insights, using the vast real-time data stores that they have accumulated through the years, using data mining techniques. Health sector, has an extremely large source of digital data - patient-health related data-store, which can be effectively used for predictive analytics. This data, may consists of missing, incorrect and sometimes incomplete values sets that can have a detrimental effect on the decisions that are outcomes of data analytics. Using the PIMA Indians Diabetes dataset, we have proposed an efficient imputation method using a hybrid combination of CART and Genetic Algorithm, as a preprocessing step. The classical neural network model is used for prediction, on the preprocessed dataset. The accuracy achieved by the proposed model far exceeds the existing models, mainly because of the soft computing preprocessing adopted. This approach is simple, easy to understand and implement and practical in its approach. 1 Introduction Real time information, based on transactional data store, differentiated the com- petitive business organizations from others in their field, enabling them to make timely and relevant business decisions. This no longer holds good. Today, the ability to forecast the direction of the business trends, to predict the effect of the various variables involved in the complex situations, allowing business organiza- tions to make proactive, knowledge driven decisions, is what differentiates the leaders in the business organizations. Gartner, Inc. has revealed that by 2012, business units will devote at least 40 % of their total budget for business intelli- gence. This is so, as they foresee the impact of the current economic turndown, to result in under-investment of information infrastructure and business tools, required to make informed and responsive decisions. Predictive modeling is a part of business intelligence. Data Mining refers to knowledge discovery in large and complex volumes of data. Soft computing involves information processing, with methods aimed to exploit the data tolerance for imprecision, uncertainty, approximate reasoning