This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random\nforest ensemble machine-learning method trained with a simple sampling scheme. This study\nhas been carried out in two major phases: feature selection and classification. In the first stage, a\nnumber of discriminative features out of 18 were selected using PSO and several feature selection\ntechniques to reduce the features dimension. In the second stage, we applied the random forest\nensemble classification scheme to diagnose lymphatic diseases. While making experiments with\nthe selected features, we used original and resampled distributions of the dataset to train random\nforest classifier. Experimental results demonstrate that the proposed method achieves a remarkable\nimprovement in classification accuracy rate.
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