ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access

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


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

   Subscribe/Renew Journal


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
Subscription Login to verify subscription
User
Notifications
Font Size

  • D. Tomar and S. Agarwal, “A Survey on Data Mining Approaches for Healthcare”, International Journal of BioScience and Bio-Technology, Vol. 5, No. 5, pp. 241-266, 2013.

  • M. Saberi-Karimian and M. Ghayour-Mobarhan, “Potential Value and Impact of Data Mining and Machine Learning in Clinical Diagnostics”, Critical Reviews in Clinical Laboratory Sciences, Vol. 58, No. 4, pp. 275-296, 2021.

  • S. Vijiyarani and S. Sudha, “Disease Prediction in Data Mining Technique-A Survey”, International Journal of Computer Applications and Information Technology, Vol. 2, No. 1, pp. 17-21, 2013.

  • M.H.B.M. Adnan and F. Damanhoori, “A Survey on Utilization of Data Mining for Childhood Obesity Prediction”, Proceedings of Asia-Pacific Symposium on Information and Telecommunication Technologies, pp. 1-6, 2010.

  • D. Piedra, A. Ferrer and J. Gea, “Text Mining and Medicine: Usefulness in Respiratory Diseases”, Archivos De Bronconeumología (English Edition), Vol. 50, No. 3, pp. 113-119, 2014.

  • H. Baek, M. Cho and S. Yoo, “Analysis of Length of Hospital Stay using Electronic Health Records: A Statistical and Data Mining Approach”, PloS One, Vol. 13, No. 4, pp. 1-14, 2018.

  • A.S. Monto and B.M. Ullman, “Acute Respiratory Illness in an American Community: the Tecumseh Study”, Jama, Vol. 227, No. 2, pp. 164-169, 1974.

  • P. Ahmad, S. Qamar and Q.A. Rizvi, “Techniques of Data Mining in Healthcare: A Review”, International Journal of Computer Applications, Vol. 120, No. 15, pp. 1-16, 2015.

  • M. Mozaffarinya, A.R. Shahriyari and G. Vahedi, “A Data-Mining Algorithm to Assess Key Factors in Asthma Diagnosis”, Revue Française d'Allergologie, Vol. 59, No. 7, pp. 487-492, 2019.

  • C.E. Wheelock, V.M. Goss and P.J. Skipp, “Application of Omics Technologies to Biomarker Discovery in Inflammatory Lung Diseases”, European Respiratory Journal, Vol. 42, No. 3, pp. 802-825, 2013.

  • Y. Feng, Y. Wang and H. Mao, “Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease”, International Journal of Medical Sciences, Vol. 18, No. 13, pp. 2871-2879, 2021.

  • M. Anthimopoulos and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases using a Deep Convolutional Neural Network”, IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp. 1207-1216, 2016.

  • A. Srivastava, S. Jain and K. Kotecha, “Deep Learning based Respiratory Sound Analysis for Detection of Chronic Obstructive Pulmonary Disease”, PeerJ Computer Science, Vol. 7, pp. 1-13, 2021.

  • K.S. Alqudaihi, N. Aslam and M.S. Alshahrani, “Cough Sound Detection and Diagnosis using Artificial Intelligence Techniques: Challenges and Opportunities”, IEEE Access, Vol. 9, pp. 102327-102344, 2021.

  • E. Oostveen, D. MacLeod and F. Marchal, “The Forced Oscillation Technique in Clinical Practice: Methodology, Recommendations and Future Developments”, European Respiratory Journal, Vol. 22, No. 6, pp. 1026-1041, 2003.


Abstract Views: 247

PDF Views: 103




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

Abstract Views: 247  |  PDF Views: 103

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