Abstract
Telemedicine is a blooming field with inter-disciplinary research and wide area of application refinement. Various techniques are proposed since last decade with a primary focus of improving telemedicine. The algorithms are either data dependent or application centric. In this paper, a multi-objective optimal medical (MooM) data processing technique is proposed under multi-dimensional data types of medial samples such as text files, image files, log files, electronic health records (EHR), audio signal files and graphic files. The technique proposes a dedicated methodology for independent data-type processing to retrieve on a standard protocol platform for transmission of data via telemedicine channel. The technique uses unsupervised and hybrid clustering approaches of machine learning to predict data-types attributes for processing, thus resulting in higher-order accuracy and data scalability on transmission channel of telemedicine environment. The MooM technique processed on medical images retrieve the compressed stream of data frames with QoS recorded 9.23, for medical textural data the QoS is 9.87 and audio signal pattern data, the QoS is recorded 9.76 on a scale of 10.
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30 May 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-03980-0
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-03980-0"
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Ahmed, S.T., Sankar, S. & Sandhya, M. RETRACTED ARTICLE: Multi-objective optimal medical data informatics standardization and processing technique for telemedicine via machine learning approach. J Ambient Intell Human Comput 12, 5349–5358 (2021). https://doi.org/10.1007/s12652-020-02016-9
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DOI: https://doi.org/10.1007/s12652-020-02016-9