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

Abstract

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.

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

“…Like most metaheuristic algorithms, WOA has been fundamentally developed for continuous search spaces; however, many complex real-world optimization problems with discrete search spaces exist [ 123 ]. Hence, the binarization approach maps continuous search space to a binary using binarization techniques [ 124 ].…”

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

“…In fact, FS is a preprocessing step in which redundant, irrelevant, and ineffective features are omitted because they do not contribute to the accuracy of the data mining model [27,28]. The technique has gained in popularity in recent years for numerous data mining applications in science [29][30][31], engineering [32][33][34], and medical diagnostics [35][36][37]. FS is a binary optimization problem in which the search agents are confined to binary (0, 1) values only, with the length of the vector depending on the number of features in the dataset, and these values correspond to selected and unselected features [7].…”

A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases

“…The classifier was trained using data from 2009 and 2010, then tested and evaluated on data from 2011. Likewise, Park et al [ 33 – 40 ] used chest X-ray images for the detection of Mycobacterium Tuberculosis bacteria in it. The researchers collected data of 3314 patients who are infected with TB bacteria and those who are normal.…”