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HSVNN: an efficient medical data classification using dimensionality reduction combined with hybrid support vector neural network

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Abstract

Early diagnosis and therapy are the most essential strategies to prevent deaths from diseases, such as cancer, brain tumors, and heart diseases. In this regard, information mining and artificial intelligence approaches have been valuable tools for providing useful data for early diagnosis. However, high-dimensional data can be challenging to examine, practically difficult to visualize, and costly to measure and store. Transferring a high-dimensional portrayal of the data to a lower-dimensional one without losing important information is the focal issue of dimensionality reduction. Therefore, in this study, dimensionality reduction-based medical data classification is presented. The proposed methodology consists of three modules: pre-processing, dimension reduction using an adaptive artificial flora (AAF) algorithm, and classification. The important features are selected using the AAF algorithm to reduce the dimension of the input data. From the results, a dimension-reduced dataset is obtained. The reduced data are then fed as input to the hybrid classifier. A hybrid support vector neural network is proposed for classification. Finally, the effectiveness of the proposed method is analyzed in terms of different metrics, namely accuracy, sensitivity, and specificity. The proposed method is implemented in MATLAB.

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Authors and Affiliations

  1. RMK College of Engineering and Technology, Chennai, Tamil Nadu, India

    P. N. Senthil Prakash

  2. KGiSL Institute of Technology, Coimbatore, Tamil Nadu, India

    N. Rajkumar

Authors
  1. P. N. Senthil Prakash
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  2. N. Rajkumar
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Correspondence to P. N. Senthil Prakash.

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Prakash, P.N.S., Rajkumar, N. HSVNN: an efficient medical data classification using dimensionality reduction combined with hybrid support vector neural network. J Supercomput 78, 15439–15462 (2022). https://doi.org/10.1007/s11227-022-04500-9

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  • DOI: https://doi.org/10.1007/s11227-022-04500-9

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