An Innovative Approach for Imputation and Classification of Medical Records for Efficient Disease Prediction
Many informative aspects of medical datasets may be extracted from comparative study of features discriminative power. Recently, consensus feature rankings have been proposed to achieve robust, unbiased and reliable rankings of attributes. We have studied the effect of classifier inclusion in a consensus feature ranking method for a medical dataset with missing values and class imbalanced data. Ability of consensus feature rankings to demonstrate superior performance with unseen classifiers is also studied in this paper.
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