Feature subset selection is of immense importance in the field of data mining. The increased dimensionality of data makes testing and training of general classification method difficult. Mining on the reduced set of attributes reduces computation time and also helps to make the patterns easier to understand. In this paper a wrapper approach for feature selection is proposed. As a part of feature selection step we used wrapper approach with Genetic algorithm as random search technique for subset generation ,wrapped with different classifiers/ induction algorithm namely decision tree C4.5, NaïveBayes, Bayes networks and Radial basis function as subset evaluating mechanism on four standard datasets namely Pima Indians Diabetes Dataset, Breast Cancer, Heart Stat log and Wisconsin Breast Cancer. Further the relevant attributes identified by proposed wrapper are validated using classifiers. Experimental results illustrate, employing feature subset selection using proposed wrapper approach has enhanced classification accuracy.

Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. Model selection for a neural network entails various factors such as selection of the optimal number of hidden nodes, selection of the relevant input variables and selection of optimal connection weights. This paper presents the application of hybrid model that integrates Genetic Algorithm and Back

M edical data mining has enormous potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized by the physicians to improve clinical diagnosis. Feature subset selection is one of data preprocessing step, which is of immense importance in the field of data mining. As a part of feature subset selection step of data preprocessing, a filter approach with genetic algorithm (GA) and Correlation based feature selection has been used in a cascaded fashion. GA rendered global search of attributes with fitness evaluation effected by CFS. Experimental results signify that the feature subset recognized by the proposed filter GA+CFS, when given as input to five classifiers, namely decision tree, Naïve Bayes, Bayesian, Radial basis function and k-nearest neighbor classifiers showed enhanced classification accuracy. Experiments have been carried out on four medical data sets publicly available at UCI.

Exudates are one of the primary signs of diabetic retinopathy, which is a main cause of blindness and can be prevented with an early screening process. In this paper, authors have attempted to detect exudates using back propagation neural network. The publicly available diabetic retinopathy dataset DIARETDB1 has been used in the evaluation process. To prevent the optic disk from interfering with exudates detection, the optic disk is eliminated. Significant features are identified from the images after preprocessing by using two methods: Decision tree and GA-CFS method are used as input to the BPN model to detect the exudates and non-exudates at pixel level. The results prove that, BPN performance with features identified by Decision tree and GA_CFS approach has outperformed the performance of BPN with all inputs. The BPN classifier best performance was found with Sensitivity of 96.97 %, Specificity of 100% and classification accuracy of 98.45%.

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This paper illustrates the applications of various ensemble methods for enhanced classification accuracy. The case in point is the Pima Indian Diabetic Dataset (PIDD). The computational model comprises of two stages. In the first stage, k-means clustering is employed to identify and eliminate wrongly classified instances. In the second stage, a fine tuning in the classification was effected. To do this, ensemble methods such as AdaBoost, bagging, dagging, stacking, decorate, rotation forest, random subspace, MultiBoost and grading were invoked along with five chosen base classifiers, namely support vector machine (SVM), radial basis function network (RBF), decision tree J48, naïve Bayes and Bayesian network. The k-fold cross validation technique is adopted. Computational experiments with the proposed method showed an improvement of 16.14% to 22.49% in the classification accuracy compared to literature survey. Among the ensemble methods tried, MultiBoost ensemble with SVM classifier and grading ensemble with naïve Bayes showed the best performance followed by MultiBoost, stacking and grading ensemble with Bayesian classifier, rotation forest ensemble with RBF and grading and rotation forest ensemble with J48. This investigation conclusively proves the significance of cascading k-means clustering with ensemble methods in the enhanced accuracy in categorization of diabetic dataset.