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  2. Volume 7, Issue 11
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Online ISSN: 2515-8260

Volume7, Issue11

A Hybrid multi-level disease filtering framework using biomedical documents and ICD drug discovery

    Konda sreenu Dr B.Raja Srinivasa reddy

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 11, Pages 7376-7386

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Abstract

Multi-level disease prediction plays a vital role in the drug to disease discovery process. Most of the conventional models use static parameters and filtering approaches in order to filter the high dimensional feature space due to high computational time and memory. Also, these models are having less accuracy and high error rate for the classification models. In order to overcome these issues, a hybrid filtering method is proposed in order to optimize the data preprocessing and feature extraction on the high dimensional dataset. Experimental results proved that the hybrid data filtering and feature extraction models have better efficiency in terms of classification accuracy and runtime(ms) than the conventional models.
Keywords:
    Biomedical documents gene-disease entities ICD codes
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(2021). A Hybrid multi-level disease filtering framework using biomedical documents and ICD drug discovery. European Journal of Molecular & Clinical Medicine, 7(11), 7376-7386.
Konda sreenu; Dr B.Raja Srinivasa reddy. "A Hybrid multi-level disease filtering framework using biomedical documents and ICD drug discovery". European Journal of Molecular & Clinical Medicine, 7, 11, 2021, 7376-7386.
(2021). 'A Hybrid multi-level disease filtering framework using biomedical documents and ICD drug discovery', European Journal of Molecular & Clinical Medicine, 7(11), pp. 7376-7386.
A Hybrid multi-level disease filtering framework using biomedical documents and ICD drug discovery. European Journal of Molecular & Clinical Medicine, 2021; 7(11): 7376-7386.
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