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An Effective Data Mining Technique to Identify and Classify Respiratory Diseases in Children and Adults


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1 Department of Computer Science and Applications, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, India
 

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The authors of this study have built an outstanding data mining model for the classification of respiratory issues in children and adults, which they have applied in their research. Deep learning ensembles are built by utilising support vector regression (SVR), long short-term memory neural networks (LSTMs), and a metaheuristic optimization (MHO) strategy that incorporates nonlinear learning in the DL ensemble. A collection of LSTMs with variable hidden layers and neurons is used to detect and exploit the underlying relationships in order to overcome the limitations of a single deep learning approach limited generalisation skills and robustness when faced with diverse input. The LSTM classification is then combined with a nonlinear-learning SVR and MHO to optimise the top-layer parameters. Nonlinear-learning meta-layer and LSTM classification. Finally, the final classification of the ensemble is provided by the fine-tuning meta-layer. Using data from six benchmark studies as well as energy consumption data sets, the proposed EDL is put to the test in two classification scenarios: ten-ahead and one-ahead classification.

Keywords

Data Mining, Ensemble Deep Learning, Support Vector Regression, Metaheuristic Optimization Algorithm
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Abstract Views: 326

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  • An Effective Data Mining Technique to Identify and Classify Respiratory Diseases in Children and Adults

Abstract Views: 326  |  PDF Views: 137

Authors

K. Nithyanandan
Department of Computer Science and Applications, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, India
S. Prakasam
Department of Computer Science and Applications, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, India

Abstract


The authors of this study have built an outstanding data mining model for the classification of respiratory issues in children and adults, which they have applied in their research. Deep learning ensembles are built by utilising support vector regression (SVR), long short-term memory neural networks (LSTMs), and a metaheuristic optimization (MHO) strategy that incorporates nonlinear learning in the DL ensemble. A collection of LSTMs with variable hidden layers and neurons is used to detect and exploit the underlying relationships in order to overcome the limitations of a single deep learning approach limited generalisation skills and robustness when faced with diverse input. The LSTM classification is then combined with a nonlinear-learning SVR and MHO to optimise the top-layer parameters. Nonlinear-learning meta-layer and LSTM classification. Finally, the final classification of the ensemble is provided by the fine-tuning meta-layer. Using data from six benchmark studies as well as energy consumption data sets, the proposed EDL is put to the test in two classification scenarios: ten-ahead and one-ahead classification.

Keywords


Data Mining, Ensemble Deep Learning, Support Vector Regression, Metaheuristic Optimization Algorithm

References