[PDF] Top 20 Title: COMPARATIVE STUDY ON DIFFERENT CLASSIFICATION TECHNIQUES FOR BREAST CANCER DATASET

... three classification techniques such as J48, MLP and Rough set were used to evaluate the percentage of accuracy with and without feature selection techniques for breast cancer effective ... See full document

... The dataset contains n number of attributes. Usually data is represented by L dimension vector. L represents the total number of features. All the features are not so useful. Instead of using all the features, we ... See full document

... of cancer deaths in women worldwide. Classification plays a vital role in data mining ...mining techniques have been extensively applied for breast cancer ...of cancer and ... See full document

... Abstract: Breast cancer is the most common invasive cancer in women and the second main cause of cancer death in females, which can be classified Benign or ...on breast cancer ... See full document

... Mining Classification Techniques on Public Health ...proposed study focused on the application of various data mining classification techniques using different machine learning ... See full document

... explore different datasets relating to Breast cancer by applying data mining ...mining techniques are applied and important data is extricated from enormous amount of data, it shall be useful ... See full document

... to study the applicability of machine learning algorithms that try to predict the breast cancer type based on the data ...from cancer patient. The aim of this study is to compare ... See full document

... in cancer detection and diagnosis (Canul-Reich et ...Using different gene expression forms with normal expression profile, irregularity could be recognized and treated before it develops abnormalities in ... See full document

... effective breast cancer prediction methods using data ...the breast cancer data warehouses for the efficient prediction of breast ...of Breast Cancer ...available ... See full document

... The comparative results demonstrate performance factors which include efficiency, specificity, sensitivity, accuracy, and ROC and mean square error values by considering 320 real time brain volume ...out ... See full document

... The Bayesian network classifier uses the unsupervised learning algorithm, where the class target is unknown though we have the inputs (attributes) [14] and the classifier learning algorithm can be structured into two ... See full document

... a comparative study of several classification techniques on the basis of their performance parameters using spam ...the different classifiers is measured with different ratio of ... See full document

... Wisconsin Breast Cancer Dataset has been heavily cited as a benchmark dataset for classifica- ...Network techniques such as Neural Networks, Probabilistic Neural Networks, and Re- ... See full document

... a study on each of ...integrates techniques from various fields including machine learning, Network intrusion detection, spam filtering, artificial intelligence, statistics and pattern recognition for ... See full document

... visual study of dendrograms to analyse the relation among the gene ...clustering techniques in the cancer profile with respect to genes data ...subsequent study further subdivided the luminal ... See full document

... FPR is called as Fall-Out & calculated as 1 – Specificity. The ROC curve shows the graphical plot between sensitivity (TPR) and 1-specificity (FPR). These two figures vary in relation to each other (determined by ... See full document

... Results were expressed as the mean ± SE. For both groups, we compared the changes in markers, L-BMD, and H-BMD, at each time point compared with pre-treatment levels using the Bonferroni correction method for multiple ... See full document

... group. Classification is the process of finding the model or function to predict the class of object whose class label is ...unknown. Classification is a supervised learning ...is Classification: a ... See full document

... 5 Zan Huang, Hsinchun Chena, Chia-Jung Hsu, Wun-Hwa Chen and Soushan Wu. 2004. Credit rating analysis with support vector machines and neural networks: a market comparative study,” Decision Support Systems ... See full document

... Figure 3 KLF16 silencing inhibited breast cancer cell proliferation in vitro and in vivo. MCF-7 and MDA-MB-231 cells were transfected with two different KLF16-targeting shRNAs or control shRNA ... See full document