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KDSAE: Chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network

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Abstract

In recent times, Chronic Kidney Disease (CKD) has affected more than 10% of the population worldwide and millions of people die every year. So, early-stage detection of CKD could be beneficial for increasing the life expectancy of suffering patients and reducing the treatment cost. It is required to build such a multimedia driven model which can help to diagnose the disease efficiently with higher accuracy before leading to worse conditions. Various techniques related to conventional machine learning models have been used by researchers in the past time without involvement of multimodal data-driven learning. This research paper offers a novel deep learning framework for chronic kidney disease classification using stacked autoencoder model utilizing multimedia data with a softmax classifier. The stacked autoencoder helps to extract the useful features from the dataset and then a softmax classifier is used to predict the final class. It has experimented on UCI dataset which contains early stages of 400 CKD patients with 25 attributes, which is a binary classification problem. Precision, recall, specificity and F1-score were used as evaluation metrics for the assessment of the proposed network. It was observed that this multimodal model outperformed the other conventional classifiers used for chronic kidney disease with a classification accuracy of 100%.

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Acknowledgments

This work was supported in part by the Indian Council of Social Science Research under grant No.02/138/2017-18/RP/ Major. The authors would like to thank the reviewers in advance for their comments and suggestions.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Phagwara, India

    Aditya Khamparia & Gurinder Saini

  2. Department of Computer Science and IT, BabaSaheb Bhim Rao Ambedkar University, Lucknow, India

    Babita Pandey

  3. Division of Examinations, Lovely Professional University, Phagwara, India

    Shrasti Tiwari

  4. Maharaja Agrasen Institute of Technology, MAIT, Delhi, India

    Deepak Gupta & Ashish Khanna

Authors
  1. Aditya Khamparia
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  2. Gurinder Saini
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  3. Babita Pandey
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  4. Shrasti Tiwari
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  5. Deepak Gupta
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  6. Ashish Khanna
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Correspondence to Deepak Gupta.

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Khamparia, A., Saini, G., Pandey, B. et al. KDSAE: Chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network. Multimed Tools Appl 79, 35425–35440 (2020). https://doi.org/10.1007/s11042-019-07839-z

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  • DOI: https://doi.org/10.1007/s11042-019-07839-z

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