Modified Monarch Butterfly Based Feature Selection for Multi Medical Data Classification Using Deep Neural Network
N. Balakumar1, B. Prabadevi2

1N. Balakumar, Department of Computer Applications, Pioneer College of Arts and Science, Coimbatore (Tamil Nadu), India.
2B. Prabadevi, Department of Computer Applications, Pioneer College of Arts and Science, Coimbatore (Tamil Nadu), India.
Manuscript received on 02 May 2019 | Revised Manuscript received on 14 May 2019 | Manuscript Published on 23 May 2019 | PP: 160-168 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F10260476S519/2019©BEIESP
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Abstract: In healthcare informatics, the individual’s disease prediction and its classification are essential. With the utilization of data mining systems, we can analyze the disease at a beginning stage and enhances the patient’s survival rate. But still, it has some issues like removing missing values and feature selection from the medical datasets. To overcome that, optimal features are selected from the datasets by the use of an innovative optimization algorithm. In the proposed work, multi-datasets (Liver, Lung, Heart, and Thyroid) are considered for the disease prediction analysis. Initially, the missing values from the input datasets are removed during the preprocessing stage. Next, to that, the optimal features are chosen by the algorithm called Modified Monarch Butterfly Optimization (MMBO). Based on the selected features, the data are classified into two norms: healthy and non-healthy by the proposed classifier i.e. Deep Neural Network (DNN). The performances of the proposed algorithm and classifier are tested on the multi-datasets in terms of sensitivity, specificity, and accuracy. The results demonstrate the MMBO-DNN algorithm achieves high accuracy of and less execution time compared to existing algorithms.
Keywords: Medical Data Classification, Missing Values, Optimal Feature Selection, MMBO, and DNN Classifier.
Scope of the Article: Data Analytics