Studies on Design of Ensembles for Efficient Learning of Diabetes Dataset

Abstract

Even though there are many factors influencing the final results of the predicting exercises, we should be very careful enough to select the list of most important ones especially in the context like diagnosing diabetes diseases. We focus on dataset size, cost sensitiveness, regional influence, and prioritization of the features. Mining the data sets of different sizes or different regions many times need not yield similar results with expected maximum accuracy. Hence the data size or inherent regional characteristics act as important parameters for mining exercises. In this research studies firstly we consider data sets from two different geographical regions and the calculation of performance measures separately. Also, we get the same for integrated data set obtained by the union of the original sets independently as inverse results establishing the hypothesis for integrated data set. Secondly we consider the issue of mechanizing the prediction of new patients heart disease diagnosis based on data mining on historical data is an extremely useful tool. newlineResearch in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models. Ensemble Methods began about fifteen years ago as a separate research area within machine learning and were motivated by the idea of wanting to leverage the power of multiple models and not just trust one model built on a small training set. Significant theoretical and experimental developments have occurred over the past fifteen years and have led to several methods, especially bagging and boosting, being used to solve many real problems. However, ensemble methods also appear to be applicable to current and upcoming problems of distributed data mining, online applications, and others. Therefore, practitioners in data mining should stay tuned for further developments in the vibrant area of ensemble methods. We apply this type of ensemble design for the critical data set like that for Diabetes. newline newline