Deep graph neural network with fish-inspired task allocation algorithm for heart disease diagnosis

Krishna Lava Kumar Gopu; Suthendran Kannan

doi:10.59796/jcst.V13N2.2023.1753

Authors

Krishna Lava Kumar Gopu Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, 501401, India
Suthendran Kannan Department of Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, Tamil Nadu 626126, India

DOI:

https://doi.org/10.59796/jcst.V13N2.2023.1753

Keywords:

deep graph neural network, feature selection, heart disease, machine learning, SMOT

Abstract

Heart disease is a very hazardous disease and many people suffered from this disease globally. The major aim is “to diagnose the heart disease with higher accuracy through decreasing error rate including computational complexity”. The existing techniques did not proffer adequate accuracy and also increased the error rate. Therefore, a Deep Graph Neural Network with Fish-Inspired Task Allocation Algorithm is proposed in this manuscript for categorizing heart disease diagnosis (DGNN-FITA-HDD). Synthetic Minority Oversampling and standard scalar strategies are utilized for pre-processing process. The pre-processed output is given to feature selection process. Two-Stage Feature Selection method selects the most important features from pre-processing output. Extracted features are transferred to Deep Graph Neural Network (DGNN) for categorizing presence and absence of heart disease. DGNN does not expose any adoption of optimization strategies for calculating the optimum parameters to assure accurate prediction. Fish-Inspired Task Allocation approach is proposed for optimizing the weight parameters of DGNN. The proposed approach is executed at MATLAB. The performance of algorithm is analyzed with/without feature selection method. By this, the proposed DGNN-FITA-HDD method attains higher accuracy with feature selection of 13.41%, 18.53%, 10.38% and 9.31% and without feature selection attains 6.5%, 8.64%, 4.39%, and 10.28% compared with existing methods, like EDGA-AHHO-HDD, XGB-MAPO-HDD, AGAFL-HDD and RFBM-HDD respectively.

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