Title: Evolutionary optimisation with outlier detection-based deep learning model for biomedical data classification

Authors: R. Raja; B. Ashok

Addresses: Department of Computer and Information Science, Annamalai University, Annamalai Nagar – 608 002, Tamil Nadu, India ' Department of Computer Science, PSPT MGR Government Arts and Science College, Sirkali, Tamil Nadu, India

Abstract: In recent times, large amount of medical data is being generated by various sources such as test reports, medications, etc. Due to the recent advances of machine learning (ML) and deep learning (DL) models, medical data classification (MDC) remains a crucial process in the healthcare sector. This study introduces a new hyperparameter tuned convolutional neural network-recurrent neural network (HPT-CNN-RNN) model for medical data classification. The proposed HPT-CNN-RNN model includes pre-processing step to transform the actual healthcare data into useful format. Besides, SVM-SMOTE approach was executed to handle the class imbalance problems. In addition, outlier detection process is performed using extreme gradient boosting (XGBoost) model. Moreover, bacterial foraging optimisation algorithm (BFOA) with CNNRNN model is employed to categorise medical data. Furthermore, the BFOA is utilised to optimally choose the hyperparameter values of the CNNRNN model. The experimental outcomes designated the better performance of the HPT-CNN-RNN model over the other methods.

Keywords: classification; medical data; data mining; outlier detection; class imbalance; deep learning; parameter tuning.

DOI: 10.1504/IJNVO.2022.127606

International Journal of Networking and Virtual Organisations, 2022 Vol.27 No.2, pp.143 - 162

Received: 23 Mar 2022
Accepted: 07 Jul 2022
Published online: 12 Dec 2022 *

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