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

Multimedia tools and applications, 2020-12, Vol.79 (47-48), p.35425-35440 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;Multimedia Tools and Applications is a copyright of Springer, (2019). All Rights Reserved. ;Springer Science+Business Media, LLC, part of Springer Nature 2019. ;ISSN: 1380-7501 ;EISSN: 1573-7721 ;DOI: 10.1007/s11042-019-07839-z

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  • Title:
    KDSAE: Chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network
  • Author: Khamparia, Aditya ; Saini, Gurinder ; Pandey, Babita ; Tiwari, Shrasti ; Gupta, Deepak ; Khanna, Ashish
  • Subjects: Classification ; Classifiers ; Computer Communication Networks ; Computer Science ; Data Structures and Information Theory ; Datasets ; Feature extraction ; Kidney diseases ; Life expectancy ; Machine learning ; Multimedia ; Multimedia Information Systems ; Scientific papers ; Special Purpose and Application-Based Systems
  • Is Part Of: Multimedia tools and applications, 2020-12, Vol.79 (47-48), p.35425-35440
  • Description: 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%.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 1380-7501
    EISSN: 1573-7721
    DOI: 10.1007/s11042-019-07839-z
  • Source: ProQuest Central
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