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Improved versions of snake optimizer for feature selection in medical diagnosis: a real case COVID-19

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Abstract

Classification of medical data is largely dependent on the effective identification of key features of the data that can be used to aid in the diagnosis of related diseases. This goal can be achieved through feature selection methods that endeavor to get rid of redundant and irrelevant features to ameliorate classification accuracy. This is the aim of this work where a new meta-heuristic, referred to as snake optimizer, was adopted for the purpose of boosting the performance of existing feature selection methods. This optimizer may smoothly fall into local optimal solutions, which may present weak search performance and slow convergence speeds in handling feature selection problems. On this basis, this paper presents three improved adaptive versions of this optimizer, each of which has increased search performance over the basic optimizer. This optimizer was improved using three mathematical models named exponential, power, and delayed S-shaped, to create three methods, referred to as exponential, power, and delayed S-shaped snake optimizers, respectively. These proposed versions were also matured to have more balance between exploration and exploitation aspects. Then, binary variants of these optimizers were evolved to solve feature selection problems using the k-nearest neighbor classifier. To verify the efficacy of these binary optimizers, 24 datasets were used, and then compared with other feature selection optimizers. The experimental results obviously manifested the efficiency of the proposed optimizers in realizing the optimal feature set by achieving utmost accuracy and minimal number of features in the majority of the studied datasets. The proposed binary power snake optimizer outperformed all other competitors in 13, 10, 8, 8, and 12 datasets in respect of classification accuracy, number of chosen attributes, specificity, sensitivity, and fitness scores, respectively. Out of the 24 datasets taken into consideration, the results on 12, 6, and 8 datasets, respectively, showed that this proposed optimizer presented performance scores of more than 90% in respect of sensitivity, accuracy, and specificity metrics.

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Data availability

The used datasets in this research are well-known datasets that are available online to the researchers.

Notes

  1. https://jundongl.github.io/scikit-feature/datasets.html.

References

  • Abed MM, Belal A-K, Mohammed Y, Mostafa Salama A, Seifedine K, Hameed AK, Begonya G-Z (2022) Novel crow swarm optimization algorithm and selection approach for optimal deep learning COVID-19 diagnostic model. Comput Intell Neurosci 2022:1307944

    Google Scholar 

  • Abouelmagd LM, Shams MY, El-Attar NE, Hassanien AE (2022) Feature selection based coral reefs optimization for breast cancer classification. In: Medical informatics and bioimaging using artificial intelligence. Springer, pp 53–72

  • Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H (2021) Run beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079

    Google Scholar 

  • Akram KM, Sihem S, Okba K (2021) Convolution neural network based marine predator algorithm for COVID-19 detection. In: 2021 International conference on information systems and advanced technologies (ICISAT). IEEE, 1–4

  • Alamir N, Kamel S, Megahed TF, Hori M, Abdelkader SM (2022) Energy management of multi-microgrid considering demand response using snake optimizer. In: 2022 23rd international middle east power systems conference (MEPCON). IEEE, pp 1–6

  • Albashish D, Hammouri AI, Braik M, Atwan J, Sahran S (2021) Binary biogeography-based optimization based SVM-RFE for feature selection. Appl Soft Comput 101:107026

    Google Scholar 

  • Aljarah I, Mafarja M, Heidari AA, Faris H, Zhang Y, Mirjalili S (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput 71:964–979

    Google Scholar 

  • Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185

    MathSciNet  Google Scholar 

  • Alweshah M, Alkhalaileh S, Al-Betar MA, Bakar AA (2022) Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowl Based Syst 235:107629

    Google Scholar 

  • Amit K, Rajneesh S (2022) Jaya-optimized fuzzy reinforcement learning classifier for COVID-19. IETE J Res. https://doi.org/10.1080/03772063.2022.2027287

    Article  Google Scholar 

  • Awadallah MA, Al-Betar MA, Braik MS, Hammouri AI, Doush IA, Zitar RA (2022a) An enhanced binary rat swarm optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection. Comput Biol Med 147:105675

  • Awadallah MA, Hammouri AI, Al-Betar MA, Braik MS, Abd EM (2022b) Binary horse herd optimization algorithm with crossover operators for feature selection. Comput Biol Med 141:105152

  • Ayça D, Ezgi KH, Ender S, Tansel D (2022) Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm. Expert Syst 39:e12949

    Google Scholar 

  • Bandyopadhyay R, Basu A, Cuevas E, Sarkar R (2021) Harris Hawks optimisation with simulated annealing as a deep feature selection method for screening of COVID-19 CT-scans. Appl Soft Comput 111:107698

    Google Scholar 

  • Braik M (2023) Enhanced Ali Baba and the forty thieves algorithm for feature selection. Neural Comput Appl 35(8):6153–6184

    Google Scholar 

  • Braik MS, Hammouri AI, Awadallah MA, Al-Betar MA, Khtatneh K (2023a) An improved hybrid chameleon swarm algorithm for feature selection in medical diagnosis. Biomed Signal Process Control 85:105073

  • Braik M, Awadallah MA, Al-Betar MA, Hammouri AI, Alzubi OA (2023b) Cognitively enhanced versions of capuchin search algorithm for feature selection in medical diagnosis: a COVID-19 case study. Cognit Comput. https://doi.org/10.1007/s12559-023-10149-0

  • Cao Y, Li Y, Zhang G, Jermsittiparsert K, Nasseri M (2020) An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm. Energy Rep 6:530–542

    Google Scholar 

  • Chen K, Zhou F-Y, Yuan X-F (2019) Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection. Expert Syst Appl 128:140–156

    Google Scholar 

  • Chen H, Wang M, Zhao X (2020) A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Appl Math Comput 369:124872

    MathSciNet  MATH  Google Scholar 

  • Cheng R, Qiao Z, Li J, Huang J (2023) Traffic signal timing optimization model based on video surveillance data and snake optimization algorithm. Sensors 23(11):5157

    Google Scholar 

  • Cheng K, Zhang J, Tian S, Liu H, Gong J, Xie Y (2022) Wifi localization algorithm based on snake optimization algorithm to optimize bp neural network. In: 2022 International conference on image processing, computer vision and machine learning (ICICML). IEEE, 615–618

  • Crow LH (1974) Reliability analysis for complex repairable systems, soc. industrial and applied mathematics, reliability and biometry. Proc Stat Anal Life Length 25:248–253

    Google Scholar 

  • Dai Y, Pang J, Li Z, Li W, Wang Q, Li S (2022) Modeling of thermal error electric spindle based on KELM ameliorated by snake optimization. Case Stud Therm Eng 40:102504

    Google Scholar 

  • Das AK, Sengupta S, Bhattacharyya S (2018) A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl Soft Comput 65:400–411

    Google Scholar 

  • Davazdahemami B, Zolbanin Hamed M, Delen D (2022) An explanatory machine learning framework for studying pandemics: the case of COVID-19 emergency department readmissions. Decis Support Syst 161:113730

    Google Scholar 

  • Dey A, Chattopadhyay S, Singh PK, Ahmadian A, Ferrara M, Senu N, Sarkar R (2021) MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features. Sci Rep 11(1):1–15

    Google Scholar 

  • Dokeroglu T, Deniz A, Kiziloz HE (2022) A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 494:1–2

    Google Scholar 

  • Elminaam DSA, Nabil A, Ibraheem SA, Houssein EH (2021) An efficient marine predators algorithm for feature selection. IEEE Access 9:60136–60153

    Google Scholar 

  • Eluri RK, Devarakonda N (2022) Binary golden eagle optimizer with time-varying flight length for feature selection. Knowl Based Syst 247:108771

    Google Scholar 

  • Eluri RK, Devarakonda N (2023) Feature selection with a binary flamingo search algorithm and a genetic algorithm. Multimed Tools Appl 82:1–52

    Google Scholar 

  • Fatih AM, Kadir S, Akif D, Fahri UM (2022) COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian optimization. Comput Biol Med 142:105244

    Google Scholar 

  • Gang H, Rui Y, Muhammad A, Guo W (2023) BEESO: multi-strategy boosted snake-inspired optimizer for engineering applications. J Bionic Eng 20:1–37

    Google Scholar 

  • Gao D, Wang G-G, Pedrycz W (2020) Solving fuzzy job-shop scheduling problem using de algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 28(12):3265–3275

    Google Scholar 

  • Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2018) Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 161:130–142

    Google Scholar 

  • Hashim Fatma A, Hussien Abdelazim G (2022) Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl Based Syst 242:108320

    Google Scholar 

  • Heidari AA, Delavar MR (2016) A modified genetic algorithm for finding fuzzy shortest paths in uncertain networks. Int Arch Photogramm Remote Sens Spat Inf Sci 41:299

    Google Scholar 

  • Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris Hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Google Scholar 

  • Jaddi NS, Abadeh MS (2022) Cell separation algorithm with enhanced search behaviour in miRNA feature selection for cancer diagnosis. Inf Syst 104:101906

    Google Scholar 

  • Jadhav S, He H, Jenkins K (2018) Information gain directed genetic algorithm wrapper feature selection for credit rating. Appl Soft Comput 69:541–553

    Google Scholar 

  • Jain I, Jain VK, Jain R (2018) Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification. Appl Soft Comput 62:203–215

    Google Scholar 

  • Jiao H, Chen H, Heidari AA, Wang M, Zhang X, Chen Y, Pan Z (2021) Orthogonal learning covariance matrix for defects of grey wolf optimizer: insights, balance, diversity, and feature selection. Knowl Based Syst 213:106684

    Google Scholar 

  • Jiaze T, Chen H, Liu J, Heidari AA, Zhang X, Wang M, Ruby R, Pham Q-V (2021) Evolutionary biogeography-based whale optimization methods with communication structure: towards measuring the balance. Knowl Based Syst 212:106642

    Google Scholar 

  • Jiaze T, Chen H, Wang M, Gandomi AH (2021) The colony predation algorithm. J Bionic Eng 18(3):674–710

    Google Scholar 

  • Kanya KL, Naga JB (2022) An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-021-01598-7

    Article  Google Scholar 

  • Khurma RA, Aljarah I, Sharieh A (2020) Rank based moth flame optimisation for feature selection in the medical application. In: 2020 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8

  • Khurma RA, Aljarah I, Sharieh A, Elaziz MA, Damaševičius R, Krilavičius T (2022) A review of the modification strategies of the nature inspired algorithms for feature selection problem. Mathematics 10(3):464

    Google Scholar 

  • Khurma RA, Albashish D, Braik M, Alzaqebah A, Qasem A, Adwan O (2023) An augmented snake optimizer for diseases and COVID-19 diagnosis. Biomed Signal Process Control 84:104718

    Google Scholar 

  • Klimov PV, Kelly J, Martinis JM, Neven H (2020) The snake optimizer for learning quantum processor control parameters. arXiv preprint arXiv:2006.04594

  • Kumar DC, Senthil AV, Jagadamba G, Musirin IB (2022) Evolving chaotic shuffled frog leaping memetic metaheuristic model-based feature subset selection for Alzheimerâ’s disease detection. In: Sustainable communication networks and application. Springer, pp 679–692

  • Kwak N, Choi C-H (2002) Input feature selection for classification problems. IEEE Trans Neural Netw 13(1):143–159

    Google Scholar 

  • Le TM, Vo TM, Pham TN, Truong SVD (2020) A novel wrapper-based feature selection for early diabetes prediction enhanced with a metaheuristic. IEEE Access 9:7869–7884

    Google Scholar 

  • Li S, Ye L (2023) Multi-level thresholding image segmentation for rubber tree secant using improved Otsuâ’s method and snake optimizer. Math Biosci Eng 20:9645–9669

    Google Scholar 

  • Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gener Comput Syst 111:300–323

    Google Scholar 

  • Li Y, Tang B, Jiao S (2023) So-slope entropy coupled with SVMD: a novel adaptive feature extraction method for ship-radiated noise. Ocean Eng 280:114677

    Google Scholar 

  • Li Z, Cheng Z, Wang Y (2022) Parameter tuning of active disturbance rejection control based on improved snake optimization algorithm. In: 2022 International conference on artificial intelligence, information processing and cloud computing (AIIPCC). IEEE, 316–322

  • Liping C, Tahereh R (2021) A new optimal diagnosis system for coronavirus (COVID-19) diagnosis based on Archimedes optimization algorithm on chest x-ray images. Comput Intell Neurosci 2021:7788491

    Google Scholar 

  • Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Alaâ’M A-Z, Mirjalili S (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl Based Syst 145:25–45

    Google Scholar 

  • Malik B, Alaa S, Hamza T, Heba A (2020) A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm. Soft Comput 25:1–26

    Google Scholar 

  • Malik B, Hussein A-Z, Mohammad R, Alaa S, Omar A (2022) Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems. Artif Intell Rev 56:1–73

    Google Scholar 

  • Mazaheri V, Khodadadi H (2020) Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm. Expert Syst Appl 161:113697

    Google Scholar 

  • Mirjalili S, Lewis A (2013) S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14

    Google Scholar 

  • Moranda PB (1975) Prediction of software reliability during debugging. In: Proceedings of the 1975 annual reliability and maintainability symposium

  • Musa JD (1975) A theory of software reliability and its application. IEEE Trans Softw Eng 1(03):312–327

    Google Scholar 

  • Nadimi-Shahraki MH, Banaie-Dezfouli M, Zamani H, Taghian S, Mirjalili S (2021) B-MFO: a binary moth-flame optimization for feature selection from medical datasets. Computers 10(11):136

    Google Scholar 

  • Nadimi-Shahraki MH, Zamani H, Mirjalili S (2022) Enhanced whale optimization algorithm for medical feature selection: a COVID-19 case study. Comput Biol Med 148:105858

    Google Scholar 

  • Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Google Scholar 

  • Qtaish A, Albashish D, Braik M, Alshammari MT, Alreshidi A, Alreshidi EJ (2023) Memory-based sand cat swarm optimization for feature selection in medical diagnosis. Electronics 12(9):2042

    Google Scholar 

  • Ragab M, Eljaaly K, Alhakamy NA, Alhadrami HA, Bahaddad AA, Abo-Dahab SM, Khalil EM (2022) Deep ensemble model for COVID-19 diagnosis and classification using chest CT images. Biology 11(1):43

    Google Scholar 

  • Rawa M (2022) Towards avoiding cascading failures in transmission expansion planning of modern active power systems using hybrid snake-sine cosine optimization algorithm. Mathematics 10(8):1323

    Google Scholar 

  • Sefer K et al (2022) Feature selection for diagnose coronavirus (COVID-19) disease by neural network and Caledonian crow learning algorithm. Appl Nanosci 13(4):3129

    Google Scholar 

  • Sheta A (2006) Reliability growth modeling for software fault detection using particle swarm optimization. In: 2006 IEEE international conference on evolutionary computation. IEEE, pp 3071–3078

  • Shiny Irene D, Beulah Rene J (2022) An efficient COVID-19 detection from CT images using ensemble support vector machine with ludo game-based swarm optimisation. Comput Methods Biomech Biomed Eng Imaging Vis 10:675–686

    Google Scholar 

  • Song S, Wang P, Heidari AA, Wang M, Zhao X, Chen H, He W, Suling X (2021) Dimension decided Harris Hawks optimization with gaussian mutation: balance analysis and diversity patterns. Knowl Based Syst 215:106425

    Google Scholar 

  • Sun G, Yang B, Yang Z, Geni X (2020) An adaptive differential evolution with combined strategy for global numerical optimization. Soft Comput 24(9):6277–6296

    Google Scholar 

  • Tao W, Jinde C, Lianglin X, Haiyang Z (2019) New stabilization results for semi-Markov chaotic systems with fuzzy sampled-data control. Complexity 2019:1–15

    MATH  Google Scholar 

  • Tao W, Xiong L, Cheng J, Xie X (2020) New results on stabilization analysis for fuzzy semi-Markov jump chaotic systems with state quantized sampled-data controller. Inf Sci 521:231–250

    MathSciNet  MATH  Google Scholar 

  • Too J, Mirjalili S (2021) A hyper learning binary dragonfly algorithm for feature selection: a COVID-19 case study. Knowl Based Syst 212:106553

    Google Scholar 

  • Wang G-G, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput Appl 31(7):1995–2014

    Google Scholar 

  • Wang H, Tan L, Niu B (2019) Feature selection for classification of microarray gene expression cancers using bacterial colony optimization with multi-dimensional population. Swarm Evolut Comput 48:172–181

  • Yamada S, Ohba M, Osaki S (1984) S-shaped software reliability growth models and their applications. IEEE Trans Reliab 33(4):289–292

    Google Scholar 

  • Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864

    Google Scholar 

  • Yao L, Yuan P, Tsai C-Y, Zhang T, Yao L, Ding S (2023) ESO: an enhanced snake optimizer for real-world engineering problems. Expert Syst Appl 230:120594

    Google Scholar 

  • Yousri R, Elbayoumi M, Soltan A, Darweesh MS (2023) A power-aware task scheduler for energy harvesting-based wearable biomedical systems using snake optimizer. Analog Integr Circuits Signal Process 115(2):183–194

    Google Scholar 

  • Yuxing L, Luqi X, Bingzhao T, Lili L, Yilan L, Xinyao G, Xiaohui X (2022) A denoising method for ship-radiated noise based on optimized variational mode decomposition with snake optimization and dual-threshold criteria of correlation coefficient. Math Probl Eng 2022:8024753

  • Zakeri A, Hokmabadi A (2019) Efficient feature selection method using real-valued grasshopper optimization algorithm. Expert Syst Appl 119:61–72

  • Zhang X, Wang D, Zhou Z, Ma Y (2019) Robust low-rank tensor recovery with rectification and alignment. IEEE Trans Pattern Anal Mach Intell 43(1):238–255

  • Zhang Y, Liu R, Wang X, Chen H, Li C (2021) Boosted binary Harris Hawks optimizer and feature selection. Eng Comput 37(4):3741–3770

    Google Scholar 

  • Zhang H, Wang Z, Chen W, Heidari AA, Wang M, Zhao X, Liang G, Chen H, Zhang X (2021) Ensemble mutation-driven salp swarm algorithm with restart mechanism: framework and fundamental analysis. Expert Syst Appl 165:113897

  • Zhao D, Liu L, Fanhua Yu, Heidari AA, Wang M, Oliva D, Muhammad K, Chen H (2021) Ant colony optimization with horizontal and vertical crossover search: fundamental visions for multi-threshold image segmentation. Expert Syst Appl 167:114122

    Google Scholar 

  • Zivkovic M, Nebojsa Bacanin K, Venkatachalam AN, Djordjevic A, Strumberger I, Al-Turjman F (2021) COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain Cities Soc 66:102669

    Google Scholar 

  • Zivkovic M, Bacanin N, Antonijevic M, Nikolic B, Kvascev G, Marjanovic M, Savanovic N (2022) Hybrid CNN and XGBoost model tuned by modified arithmetic optimization algorithm for COVID-19 early diagnostics from x-ray images. Electronics 11(22):3798

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Al-Balqa Applied University, Al-Salt, Jordan

    Malik Sh. Braik, Abdelaziz I. Hammouri & Omar A. Alzubi

  2. Department of Computer Science, Al-Aqsa University, Gaza, Palestine

    Mohammed A. Awadallah

  3. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates

    Mohammed A. Awadallah

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Mohammed Azmi Al-Betar

  5. Department of Information Technology, Al-Balqa Applied University, Irbid, Jordan

    Mohammed Azmi Al-Betar

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  1. Malik Sh. Braik
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  2. Abdelaziz I. Hammouri
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  3. Mohammed A. Awadallah
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  4. Mohammed Azmi Al-Betar
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  5. Omar A. Alzubi
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Contributions

MB: presented and developed the mathematical models of the proposed algorithms. He also developed the programs and pseudo-code of the proposed algorithms. Malik also ran statistical tests and went through the results. Last but not least, Malik thoroughly revised the entire paper. AH: executed the programs and experimental scenarios of the work. He prepared the experiments, tables, and diagrams. Abdelaziz also discussed the evaluation results of the developed algorithms and other algorithms. MA: tested the technical concepts in the paper, the readability of the overall work and English grammar. He provided English proof for it as well. MA-B: verified that the references and findings were genuine. Additionally, he provided feedback on the work and assisted with its development and analysis. OA: discussed the convergence results of the proposed algorithms. He also made revisions to the paper’s conclusion and abstract.

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Correspondence to Abdelaziz I. Hammouri.

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Braik, M.S., Hammouri, A.I., Awadallah, M.A. et al. Improved versions of snake optimizer for feature selection in medical diagnosis: a real case COVID-19. Soft Comput 27, 17833–17865 (2023). https://doi.org/10.1007/s00500-023-09062-3

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