Regression Analysis of the Prognostic Breast Cancer using Silvaco

Breast cancer is one of the main causes of female fatality all over the world and is the major field of research since quite a long time with lesser improvementthan expected. Many institutions and organizations are working in this field to lead to a possible solution of the problem or to lead to more understandingof the problem. Many previous researches were studied for better understanding of the problem and the work done already to remove redundancy andcontribute to the field, Wisconsin-Madison prognostic Breast cancer (WPBC) data set from the UCI machine learning repository was used for training of 198 individual cases by selecting best features out of 34 predictors. Feature selection model were used with silvaco softeware for feature reduction and for better classification. Different feature selection and new metod with silvaco were used to improve the accuracy of classification. Many improvements in accuracies were found out by using different approaches than the earlier studies conducted in the same field. The Naïve Bayes and Logistic Regression structure showed via 10 fold cross validation analysis improvement accordingly by usingdifferent feature selection and generation structure with these classifiers and gave better result than the best results known for these classification structure.

کلیدواژه ها:

structure ، Selection ، silvaco