Feature Selection using FFS and PCA in Biomedical Data Classification with AdaBoost-SVM

Recently, there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition. A computer aided diagnosis method, which aims higher classification accuracy, is developed to classify the biomedical dataset. This process includes two types of machine learning algorithms: feature selection and classification. In this method, firstly, features were extracted from biomedical dataset, then the extracted features were classified by hybrid AdaBoost-Support Vector Machines (SVM) classifier structure. For feature selection, Forward Feature Selection (FFS) and Principal Component Analysis (PCA) algorithms were used, and the performance of the feature selection algorithms was tested by AdaBoost-SVM classifier. Following it, advantages and disadvantages of these algorithms were evaluated. Wisconsin Breast Cancer (WBC), Pima Diabetes (PD), Heart (Statlog) biomedical datasets were taken from UCI database and Electrocardiogram (ECG) signals were taken from Physionet ECG Database, and were used to test the proposed hybrid structure. The used two hybrid structures and other studies in the literature were compared with our findings. The obtained results show that the proposed hybrid structure has high classification accuracy for biomedical data classification

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