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

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 di...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Archives of computational methods in engineering Ročník 30; číslo 3; s. 1863 - 1895
Hlavní autoři: Kaur, Sukhpreet, Kumar, Yogesh, Koul, Apeksha, Kumar Kamboj, Sushil
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Springer Nature B.V 01.04.2023
Springer Netherlands
Témata:
Algorithms
Alzheimer's disease
Artificial intelligence
Classification
Data mining
Disease
Epidemics
Feature selection
Heart diseases
Heuristic methods
Machine learning
Medical diagnosis
Optimization
Optimization techniques
Patients
Review
Search algorithms
ISSN:1134-3060, 1886-1784, 1886-1784
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:1134-3060
1886-1784
1886-1784
DOI:10.1007/s11831-022-09853-1