A SURVEY ON FEATURE SELECTION FOR CHRONIC DISEASES CLASSIFICATION SYSTEMS

The mortality rate due to chronic diseases is increasing day by day. Timely diagnosis at an early stage can help in taking appropriate measures for prevention and mitigation of these diseases along with better prognosis. In this paper we present a survey on various approaches of feature selection and classification techniques used for prediction and identification of these diseases. We shall discuss the importance of feature selection methodologies for improving the accuracy and performance of classification systems. Feature selection, a dimensionality reduction technique can help deal with the problem of “curse of dimensionality” as large data sets means huge number of records and even greater number of features and also enhance its computational efficiency. The results and performance yielded by various classification algorithms after application of these pre-processing techniques gives us promising results. IndexTerms : Data Mining, Feature Selection, Classification, Hybrid.

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