Apply AdaboostM1 and Bagging to improve the predict accuracy of Lymphatic disease

[[abstract]]In this research, the use of two committee machine AdaboostM1 and Bagging?s techniques for lymphatic diseases, to improve the predict accuracy of artificial intelligence data mining. The so-called lymphoma, also called Malignant Lymphoma. So far, the reasons for the occurrence of lymphoma is still unclear, there are many reasons may cause lymphatic disease. Therefore, the study use data mining of artificial intelligence can extracted features of the potential impact of factors from the large amounts of data, experimenting with the data from the patients who suffering from lymphatic and then established a classification model of the lymphatic. The methods used in this research, include two decision tree ID3 and C4.5, Support Vector Machine(SVM) and back-propagation neural network(BPNN), moreover this research try to use Resample techniques in the preprocessing step, further use committee machine(AdaboostM1 and Bagging) to up the accuracy of prediction, result show will compare with the past related literatures. This research indicated AdaboostM1 and Bagging can improve the accuracy of predict, however using AdaboostM1 we got the best predict accuracy(94.5946%) than Bagging from the 24 classifications