Hybrid Dragonfly Optimization-Based Artificial Neural Network for the Recognition of Epilepsy

Parthiban, K.; Vijayachitra, S.; Dhanapal, R.

doi:10.2991/ijcis.d.191022.001

Cited by 2 publications

(1 citation statement)

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“…The Binary Dragon Fly optimization method was used to select prominent features that accurately helped the cry signals' classification [ 160 ]. The authors in [ 161 ] proposed a method to detect Epilepsy, a disorder of the Central Nervous System. The algorithm was implemented on the EEG signals pre-processed by the Kalman Filter (KF) to reduce the impulse noise.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

Kaur¹,

Kumar

Koul

et al. 2022

Arch Computat Methods Eng

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There is a need for some techniques to solve various problems in today’s computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study’s primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

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Section: Related Workmentioning

confidence: 99%