Medical Data Classification Based on SMOTE and Recurrent Neural Network
P. Penchala Prasad1, F. Sagayaraj Francis2, S. Zahoor-Ul-Huq3

1P. Penchala Prasad*, CSE department, G.PullaReddyEngineering College , Kurnool, India.
2Dr. F. Sagaraj Francis, CSE department, Pondicherry Engineering College , Puducherry, India.
3Dr. S. Zahoor-Ul-Huq, CSEdepartment, G. Pulla Reddy Engineering College , Kurnool, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2560-2565 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5444029320/2020©BEIESP | DOI: 10.35940/ijeat.C5444.029320
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Abstract: Medical data classification analysis the medical data of the patients to predict the diseases risk. Data mining techniques were highly used in the medical data classification and predicted the diseases. Many existing methods were use the various classifier and feature selection to improve the performance of the classification. Although data imbalance problem is need to be solved for increases the performance. In this research, Synthetic Minority Over-sampling TEchnique (SMOTE) techniques is used for solving the data imbalance problem and Recurrent Neural Network (RNN) was used for the classification. The SMOTE method based on the k Nearest Neighbor (kNN) for the over-sample and under-sample the attributes. The RNN process the instance independent of the previous instance for the classification. Four medical datasets of University of California, Irvine (UCI) were used to evaluate the effectiveness of the proposed SMOTE-RNN method. The proposed SMOTE-RNN method has the accuracy of 85 % while existing method has 82 % accuracy.
Keywords: Data Imbalance, k Nearest Neighbor, Medical data classification, Recurrent Neural Network and Synthetic Minority Over-sampling Technique.