An Optimized Cluster Convolution based Hybrid Hierarchical Deep Learner for Chronic Kidney Disease (CKD)

An Optimized Cluster Convolution based Hybrid Hierarchical Deep Learner for Chronic Kidney Disease (CKD)

© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : P. Usha, N. Kavitha
DOI : 10.14445/22315381/IJETT-V70I11P216

How to Cite?

P. Usha, N. Kavitha, "An Optimized Cluster Convolution based Hybrid Hierarchical Deep Learner for Chronic Kidney Disease (CKD)," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 154-162, 2022. Crossref,

Chronic Kidney Disease (CKD) remains a universal health issue. Learning the attributes relevant to CKD patients might help improve the early prediction of CKD. However, the outliers in the CKD database may affect the prediction accuracy. To solve this issue, a Moth Flame (MF)-based DBSCAN with Pearson Correlation (MFDBSCAN-PC) algorithm was suggested that adjusts the DBSCAN variables during clustering of the CKD-related attributes and creates the cleansed database without outliers. Also, various machine learning classifiers were performed for CKD prediction. But, the complex and implicit temporal relationships between local and global attributes were not learned, influencing the learning of the timeseries CKD database. Therefore, this article proposes a Deep Clustering-based outlier removal and Convolutional Neural network with a Hierarchical Bi-directional Long Short-Term Memory (DC-CNN-HBLSTM) model to process the time-series database for CKD prediction. Primarily, the CKD information-rich dataset is pre-processed using data imputation to fill up any blanks in the rows. Then, MF optimization performs to select the optimal DBSCAN variables and attributes simultaneously. Based on the best variables, the DBSCAN is employed as a new clustering layer in the CNN structure to cluster the data points and remove the outliers from the database, resulting in a newly cleaned database. After that, the HBLSTM classifier is trained by learning the temporal correlation between the local and global attributes to create a trained model. Further, the trained classification model is used to classify the test instances into healthy and CKD patients. Finally, the experimental results realize the DC-CNN-HBLSTM model achieves 96.21% accuracy compared to the SVM, recursive ANN, and ELM classifiers, which achieved 88.69%, 90.57%, and 92.9% accuracy for the CKD database.

Attribute selection, Chronic kidney disease, Clustering, CNN, DBSCAN, Hierarchical bidirectional LSTM, Moth flame optimization, Outliers removal.

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