Optimizing feature selection to improve medical diagnosis |
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Authors: | Ya-Ju Fan Wanpracha Art Chaovalitwongse |
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Institution: | 1.Department of Industrial and Systems Engineering,Rutgers University,Piscataway,USA |
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Abstract: | In this paper, we propose a new optimization framework for improving feature selection in medical data classification. We
call this framework Support Feature Machine (SFM). The use of SFM in feature selection is to find the optimal group of features
that show strong separability between two classes. The separability is measured in terms of inter-class and intra-class distances.
The objective of SFM optimization model is to maximize the correctly classified data samples in the training set, whose intra-class
distances are smaller than inter-class distances. This concept can be incorporated with the modified nearest neighbor rule
for unbalanced data. In addition, a variation of SFM that provides the feature weights (prioritization) is also presented.
The proposed SFM framework and its extensions were tested on 5 real medical datasets that are related to the diagnosis of
epilepsy, breast cancer, heart disease, diabetes, and liver disorders. The classification performance of SFM is compared with
those of support vector machine (SVM) classification and Logical Data Analysis (LAD), which is also an optimization-based
feature selection technique. SFM gives very good classification results, yet uses far fewer features to make the decision
than SVM and LAD. This result provides a very significant implication in diagnostic practice. The outcome of this study suggests
that the SFM framework can be used as a quick decision-making tool in real clinical settings. |
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