Metaheuristic feature optimization using GA, PSO, and HHO for AWVs data classification

¹ Department of Information Security, Faculty of Information Technology, Posts and Telecommunications Institute of Technology, 11 Nguyen Dinh Chieu, Sai Gon ward, Ho Chi Minh City, Viet Nam

² Department of Secondary Education, Faculty of High School Program, Rick Hansen Secondary School, 1150 Dream Crest Road, Mississauga, ON, Canada

³ Department of Biostatistics and Informatics, Faculty of Public Health, Pham Ngoc Thach University of Medicine, 2 Duong Quang Trung Street, Hoa Hung Ward, Ho Chi Minh, Viet Nam

⁴ Department of Physiology, Pathophysiology, and Immunology, Faculty of Fundamental Sciences and Basic Medical Sciences, Pham Ngoc Thach University of Medicine, 2 Duong Quang Trung Street, Hoa Hung Ward, Ho Chi Minh, Viet Nam

IJOCTA, 026220101 https://doi.org/10.36922/IJOCTA026220101

Received: 31 May 2026 | Revised: 16 June 2026 | Accepted: 16 June 2026 | Published online: 1 July 2026

© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )

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Abstract

Annual Wellness Visits (AWVs) generate large volumes of clinical and laboratory data that can support predictive healthcare analytics. However, the high-dimensional nature of healthcare datasets often introduces redundant and irrelevant variables, which may negatively affect model performance and computational efficiency. This study proposes a hybrid framework that integrates metaheuristic feature selection techniques with machine learning and deep learning models to improve predictive analysis using AWVs data. Three optimization algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), were employed to identify informative feature subsets from a dataset containing 2,518 patient records and 53 clinical attributes. The selected features were subsequently used to train several machine learning classifiers and deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN) models. Experimental results demonstrate that metaheuristic optimization effectively reduces dataset dimensionality while maintaining strong predictive performance. The feature selection process successfully identified compact subsets of clinically relevant variables, leading to improved model efficiency and reduced computational complexity. Comparative analyses indicate that optimized feature subsets improve classification performance across multiple learning algorithms, while deep learning models exhibit high training capability on the AWVs dataset. The findings highlight the importance of combining feature selection with advanced predictive models to improve healthcare data analysis and support data-driven clinical decision-making. The proposed structure provides an effective approach for handling high-dimensional healthcare datasets and offers a foundation for the development of intelligent clinical decision-support systems. Future work will focus on validating the structure using larger multi-center datasets and incorporating explainable artificial intelligence techniques to improve model interpretability and clinical applicability.

Keywords

Metaheuristic algorithms

Convolution bidirectional learners

CBLs

Annual wellness visits

Metaheuristic

Funding

This work was supported by the Posts and Telecommunications Institute of Technology (PTIT). Number: 999/QD-HV.

Conflict of interest

The authors confirm that there are no conflict of interest.

References

1. Tuan NM, Duy TT, Meesad P, et al. Optimizing predictive accuracy in general medical exams using hybrid machine learning and metaheuristic optimization methods. Int J Optim Control Theor Appl. 2026;16(3):888-907. https://doi.org/10.36922/IJOCTA026020008

2. Keswani M. A comparative analysis of metaheuristic algorithms in interval-valued sustainable economic production quantity inventory models using center-radius optimization. Decis Anal J. 2024;12:100508. https://doi.org/10.1016/j.dajour.2024.100508

3. Pendokhare D, Chakraborty S. A comparative analysis of preying behavior-based metaheuristic algorithms for optimization of laser beam drilling processes. Decis Anal J. 2024;10:100412. https://doi.org/10.1016/j.dajour.2024.100412

4. Bannoud MA, Da Silva CAM, Martins TD. Applications of metaheuristic optimization algorithms in model predictive control for chemical engineering processes: A systematic review. Annu Rev Control. 2024;58:100973. https://doi.org/10.1016/j.arcontrol.2024.100973

5. Wang K, Guo M, Dai C, Li Z, Wu C, Li J. An effective metaheuristic technology of people duality psychological tendency and feedback mechanism-based Inherited Optimization Algorithm for solving engineering applications. Expert Syst Appl. 2024;244:122732. https://doi.org/10.1016/j.eswa.2023.122732

6. Zuhanda MK, Hartono, Hasibuan SARS, Napitupulu YY. An exact and metaheuristic optimization structure for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence. Decis Anal J. 2024;13:100517. https://doi.org/10.1016/j.dajour.2024.100517

7. Abatemarco A, Aria M, Beraldo S, Collaro M. Measuring health care access and the inequality: A decomposition approach. Econ Model. 2024;132:106659. https://doi.org/10.1016/j.econmod.2024.106659

8. Aluh DO, Santos-Dias M, Silva M, et al. Contextual factors influencing the use of coercive measures in Portuguese mental health care. Int J Law Psychiatry. 2023;90:101918. https://doi.org/10.1016/j.ijlp.2023.101918

9. Evenson SE, Hafferty FW, Sharp RR, Tilburt JC. Measuring and Monitoring Health Equity in Health Care Organizations: Why It's Important and How to Move Forward. Mayo Clin Proc. 2024;99(8):1212-1218. https://doi.org/10.1016/j.mayocp.2024.04.005

10. Fedewa SA, Valentino LA, Koo A, et al. Global patterns of hemophilia drug trials, hemophilia care, and health care measures. Res Pract Thromb Haemost. 2025;9(2):102714. https://doi.org/10.1016/j.rpth.2025.102714

11. Jern-Matintupa MM, Riipinen AM, Laine MK. Impact of Digital Interventions in Occupational Health Care: A Systematic Review. Mayo Clin Proc Digit Health. 2025;3(2):100216. https://doi.org/10.1016/j.mcpdig.2025.100216

12. Karimbux N, John MT, Stern A, et al. Measuring patient experience of oral health care: A call to action. J Evid Based Dent Pract. 2023;23(1):101788. https://doi.org/10.1016/j.jebdp.2022.101788

13. Kifell J, Burns KEA, Duong J, et al. Measuring family engagement in intensive care: Validation of the FAME tool. J Crit Care. 2025;87:155046. https://doi.org/10.1016/j.jcrc.2025.155046

14. Knaul FM, Arreola-Ornelas H, Kwete XJ, et al. Distributed opioids in morphine equivalent: A global measure of availability for palliative care. J Pain Symptom Manage. 2025;69(2):204-215. https://doi.org/10.1016/j.jpainsymman.2024.10.026

15. Piotrowska MJ, Sakowski K, Horn J, Mikolajczyk R, Karch A. The effect of re-directed patient flow in combination with targeted infection control measures on the spread of multi-drug-resistant Enterobacteriaceae in the German health-care system: A mathematical modelling approach. Clin Microbiol Infect. 2023;29(1):109.e1-109.e7. https://doi.org/10.1016/j.cmi.2022.08.001

16. Power L, Davidson G, Jacobs P, McCusker P, McCartan C, Devaney J. Identifying core measures to be used in mental health research with care experienced young people: A Delphi study. Child Youth Serv Rev. 2024;157:107380. https://doi.org/10.1016/j.childyouth.2023.107380

17. Ramos-Pozón S, Román-Maestre B, Blánquez B. Coercive measures in disability and mental health care services: Mechanical restraints from a bioethical and legal perspective in Spain. Int J Law Psychiatry. 2025;99:102067. https://doi.org/10.1016/j.ijlp.2024.102067

18. Tawfik D, Bayati M, Liu J, et al. Predicting Primary Care Physician Burnout From Electronic Health Record Use Measures. Mayo Clin Proc. 2024;99(9):1411-1421. https://doi.org/10.1016/j.mayocp.2024.01.005

19. Teferra AA, Wing JJ, Lu B, Xu W, Roberts ME, Ferketich AK. Examining trends in health care access measures among low-income adult smokers in Ohio: 2012-2019. Prev Med Rep. 2023;31:102106. https://doi.org/10.1016/j.pmedr.2022.102106

20. Vonnahme LA, Raykin J, Jones M, et al. Using Electronic Health Record Data to Measure the Latent Tuberculosis Infection Care Cascade in Safety-Net Primary Care Clinics. AJPM Focus. 2023;2(4):100148. https://doi.org/10.1016/j.focus.2023.100148

21. Young DL, Engels R, Colantuoni E, Friedman LA, Hoyer EH. Corrigendum to 'Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses'. Health Policy Technol. 2023;12(4):100825. https://doi.org/10.1016/j.hlpt.2023.100825

22. Zhang L, Liang H, Luo H, et al. Quality in screening and measuring blood pressure in China's primary health care: A national cross-sectional study using unannounced standardized patients. Lancet Reg Health West Pac. 2024;43:100973. https://doi.org/10.1016/j.lanwpc.2023.100973

23. Dokeroglu T, Canturk D. New Harris Hawks algorithms for the Close-Enough Traveling Salesman Problem. Intell Syst Appl. 2025;28:200586. https://doi.org/10.1016/j.iswa.2025.200586

24. Raju VAG, Nayak J, Dash PB, Mishra M. A Hybrid Multi-Head Attention and Harris Hawks Optimized Long Short-Term Memory Model for High-Precision Lithium-Ion Battery Remaining Useful Life Prediction. Appl Energy Combust Sci. 2026:100527. https://doi.org/10.1016/j.jaecs.2026.100527

25. Mahto AK, Kalam MdA, Paul K. A novel hybrid Harris hawk optimization-sine cosine algorithm for the influence of electric vehicle and renewable energy sources in power system distribution network. Frankl Open. 2025;13:100401. https://doi.org/10.1016/j.fraope.2025.100401

26. Uçar U, Macit CK, Tanyeri B, Ozgen BS, Ayık M. Pareto-based harris hawks optimization of hardness and tribological performance in ZnO–hBN reinforced aluminum matrix composites. Results Control Optim. 2026;23:100702. https://doi.org/10.1016/j.rico.2026.100702

27. Tuan NM, Meesad P. New solutions of sixth-order Benney-Luke equation using bilinear neural network method. Z Angew Math Phys. 2025;76(4):133. https://doi.org/10.1007/s00033-025-02516-8

28. Tuan NM, Meesad P, Hieu DV, Cuong NHH, Maliyaem M. On Students' Sentiment Prediction Based on Deep Learning: Applied Information Literacy. SN Comput Sci. 2024;5(7):928. https://doi.org/10.1007/s42979-024-03281-7

29. Hung TNP, Tuan NM. Transfer Learning with Particle Swarm Optimization for Durian Leaf Disease Image Classification. Appl Fruit Sci. 2026;68(3). https://doi.org/10.1007/s10341-026-01850-z

30. Tuan NM, Meesad P, Nguyen HHC. English–Vietnamese machine translation using deep learning for chatbot applications. SN Comput Sci. 2023;5(1). https://doi.org/10.1007/s42979-023-02339-2

31. Catapan SDC, Sazon H, Zheng S, et al. A systematic review of consumers' and healthcare professionals' trust in digital healthcare. NPJ Digit Med. 2025;8(1):115. https://doi.org/10.1038/s41746-025-01510-8

32. Hindhede AL, Andersen VH. AI and personalized medicine in healthcare: algorithmic normativity and practice configurations in danish healthcare education. AI Soc. 2026;41(5):4737-4750. https://doi.org/10.1007/s00146-025-02832-7

33. Hermosilla P, Soto R, Monfroy E, et al. Analysis of hybrid CNN models optimized with metaheuristic algorithms for melanoma detection. Sci Rep. 2026;16(1):13075. https://doi.org/10.1038/s41598-026-42711-9

34. Abdelhay EH, Elgamily KM, Badr WOEF. Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma. Sci Rep. 2026;16(1):15110. https://doi.org/10.1038/s41598-026-51619-3

35. Elgamily KM, Mohamed MA, Abou-Taleb AM, Ata MM. Improved object detection in remote sensing images by applying metaheuristic and hybrid metaheuristic optimizers to YOLOv7 and YOLOv8. Sci Rep. 2025;15(1):7226. https://doi.org/10.1038/s41598-025-89124-8

36. Thengvall BG, Hall SN, Deskevich MP. Measuring the effectiveness and efficiency of simulation optimization metaheuristic algorithms. J Heuristics. 2025;31(1):12. https://doi.org/10.1007/s10732-025-09549-2

37. Djafri L. Metaheuristic-based data-level rebalancing for imbalanced binary classification: an empirical study. Sci J King Faisal Univ Basic Appl Sci. 2026;27(1):5. https://doi.org/10.1007/s44523-026-00005-9

38. Gorgij AD, Kisi O, Heddam S, Vishwakarma DK, Ergun H, Külls C. Metaheuristic-optimized neuro-fuzzy models for meteorological drought prediction. Environ Earth Sci. 2026;85(7):192. https://doi.org/10.1007/s12665-026-12910-8

39. Kittipittayakorn C. Association between healthcare resources, healthcare systems, and population health in European countries. BMC Health Serv Res. 2025;25(1):720. https://doi.org/10.1186/s12913-024-11743-0

40. Virag M, Kovacs R, Marovics G, et al. Bridging healthcare gaps through specialized mobile healthcare services to improve healthcare access and outcomes in rural Hungary. Sci Rep. 2025;15(1):12692. https://doi.org/10.1038/s41598-025-97447-9

41. Portela MC, Escosteguy CC, Lima SML, et al. Healthcare gaps and inequities following hospitalisation for COVID-19 in Brazil's universal healthcare system: a patient-engaged survey of Long COVID healthcare needs, use and barriers. Int J Equity Health. 2025;24(1):275. https://doi.org/10.1186/s12939-025-02635-8

42. Ibraimova L, Newton AS, Freedman SB, et al. Healthcare Cost Associated with an Acute Mental Healthcare Bundle in Pediatric Emergency Departments. Pharmacoeconomics Open. 2026;10(2):265-274. https://doi.org/10.1007/s41669-025-00631-w

43. Elsharif SAM, Abdelraheem EMH, Salih HS, et al. Improving healthcare quality in Sudan: situation and factors influencing healthcare professionals' engagement. BMC Health Serv Res. 2025;25(1):1237. https://doi.org/10.1186/s12913-025-13481-3

44. Zhu Y, Yan X, Han Z, He S. Patient satisfaction with cross-boundary healthcare: a cross-sectional study of Hong Kong residents' healthcare utilization in mainland China. BMC Health Serv Res. 2026;26(1):300. https://doi.org/10.1186/s12913-026-14096-y

45. Xu Q, Zhang X, Xie T, et al. Evaluation of the effectiveness of social mobilization for vaccination among healthcare and non-healthcare workers in emergency situations. NPJ Vaccines. 2026;11(1):75. https://doi.org/10.1038/s41541-026-01392-1

46. Hawwash N, Lavallee J, Haque E. Hello healthcare: evaluating the impact of a healthcare conference for secondary school pupils. BMC Med Educ. 2025;25(1):1133. https://doi.org/10.1186/s12909-025-07637-2

47. Snogren M, Ek K, Lindmark U, Browall M, Eriksson I. Oral healthcare for older adults in Swedish municipal healthcare—a qualitative study of healthcare professionals' experiences. BMC Geriatr. 2025;25(1):110. https://doi.org/10.1186/s12877-025-05764-5

48. Aslan E, Özüpak Y. A hybrid deep learning structure based on VGG19 and U-Net for accurate brain tumor segmentation in MRI images. Magn Reson Lett. 2026:200275. https://doi.org/10.1016/j.mrl.2026.200275

49. Kör H, Mazman R. Brain Tumor Detection and Classification with Deep Learning Based CNN Method. Comput Syst Artif Intell. 2025;1(1):15-19. https://doi.org/10.69882/adba.csai.2025073

50. Aslan E, Özüpak Y. A hybrid SE-ResNet50 deep learning structure for high-accuracy and explainable cotton leaf disease classification. BMC Plant Biol. 2026. https://doi.org/10.1186/s12870-026-08960-6

51. Aslan E, Özüpak Y. Detection of road extraction from satellite images with deep learning method. Clust Comput. 2025;28(1):72. https://doi.org/10.1007/s10586-024-04880-y

52. Fei Z, Ryeznik Y, Sverdlov A, Tan CW, Wong WK. An overview of healthcare data analytics with applications to the COVID-19 pandemic. IEEE Trans Big Data. 2021;1. https://doi.org/10.1109/tbdata.2021.3103458

53. Hang CN, Yu PD, Chen S, Tan CW, Chen G. MEGA: Machine Learning-improved Graph Analytics for Infodemic Risk Management. IEEE J Biomed Health Inform. 2023;27(12):6100-6111. https://doi.org/10.1109/jbhi.2023.3314632

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