Volume 7 • Issue 1 • PP: 18–28 • 2026
Intelligent Healthcare Optimization Using Metaheuristic Algorithms: A Review of Emerging Methods and Applications
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Machine learning and optimization techniques have significantly changed the healthcare industry, especially in finding and managing essential and dangerous diseases like lung cancer, breast cancer, diabetes as well as heart disease. Lung cancer, which is among the common fatal cancers, requires proper subtyping before proper management is made. This has been achieved through machine learning alongside radiomics, where detailed imaging characteristics of the tumor from CT scans are retrieved without invasive procedures. In the same way, machine learning has provided much higher detection, diagnosis and treatment levels of breast cancer, diabetes and heart disease. This literature review sums up the priorities of studies showing the benefits of using machine learning and bio-inspired optimization methods to address the challenges posed by disease classification and prediction. Such complications have proved great potential in improving the diagnostic methods used for early intervention and, thereby, accurate and efficient diagnosis of a problem, developing an appropriate treatment plan and, thus, improving the patient caring methods and scenario, which has played an imperative role determining the future of modern-day health care.
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References
[1] S. Barshandeh, N. Khodadadi, B. Abdollahzadeh, A. Mohammadzadeh, E.-S. M. El-Kenawy, M. M. Eid, and K. M. Mosalam, “Gray langurs optimizer: a multigroup bio-inspired optimization algorithm,” Artificial Intelligence Review, 2026.
[2] C. Jacob, C. Gopakumar, and F. Nazarudeen, “Optimized radiomics-based machine learning approach for lung cancer subtype classification,” Biomedical Engineering: Applications, Basis and Communications, vol. 35, no. 05, p. 2350023, 2023.
[3] L. Li, J. Yang, L. Y. Por, M. S. Khan, R. Hamdaoui, L. Hussain, Z. Iqbal, I. M. Rotaru, D. Dobrot˘a, M. Aldrdery et al., “Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques,” Heliyon, vol. 10, no. 4, 2024.
[4] Y. Dou and W. Meng, “An optimization algorithm for computer-aided diagnosis of breast cancer based on support vector machine,” Frontiers in Bioengineering and Biotechnology, vol. 9, p. 698390, 2021.
[5] K. Chatra, V. Kuppili, D. R. Edla, and A. K. Verma, “Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function,” Medical & Biological Engineering & Computing, vol. 57, no. 12, pp. 2673–2682, 2019.
[6] X. Pan, T. Zhang, Q. Yang, D. Yang, J.-C. Rwigema, and X. S. Qi, “Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models,” The British journal of radiology, vol. 93, no. 1112, p. 20190825, 2020.
[7] K. E. M. R. Lomboy and R. M. Hernandez, “A comparative performance of breast cancer classification using hyper-parameterized machine learning models,” International Journal of Advanced Technology and Engineering Exploration, vol. 8, no. 82, p. 1080, 2021.
[8] S. Konda, C. Goswami, S. J, R. K, R. Yajjala, and N. S. Koti Mani Kumar Tirumanadham, “Optimizing diabetes prediction: A comparative analysis of ensemble machine learning models with pso-adaboost and aco-xgboost,” in 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), 2023, pp. 1025–1031.
[9] P. Palimkar, R. N. Shaw, and A. Ghosh, “Machine learning technique to prognosis diabetes disease: Random forest classifier approach,” in Advanced computing and intelligent technologies: proceedings of ICACIT 2021. Springer, 2021, pp. 219–244.
[10] M. Agagu, “A predictive diagnostic model for diabetes using machine learning technique,” in Smart Healthcare and Machine Learning. Springer, 2024, pp. 235–247.
[11] L. Lama, O. Wilhelmsson, E. Norlander, L. Gustafsson, A. Lager, P. Tynelius, L. Warvik, and C.-G. Ostenson, “Machine learning for prediction of diabetes risk in middle-aged swedish people,” Heliyon, vol. 7, no. 7, 2021.
[12] A. Piersanti et al., “Digital health technologies to improve diabetes prevention and optimize therapy: from model-based approaches to feature-based machine learning,” Ph.D. dissertation, Universita Politecnica delle Marche, 2024.
[13] R. Jayasudha, C. Suragali, J. Thirukrishna, and B. Santhosh Kumar, “Hybrid optimization enabled deep learning-based ensemble classification for heart disease detection,” Signal, Image and Video Processing, vol. 17, no. 8, pp. 4235–4244, 2023.
[14] R. Sonawane and H. Patil, “Automated heart disease prediction model by hybrid heuristic-based feature optimization and enhanced clustering,” Biomedical Signal Processing and Control, vol. 72, p. 103260, 2022.
[15] H. Veisi, H. R. Ghaedsharaf, and M. Ebrahimi, “Improving the performance of machine learning algorithms for heart disease diagnosis by optimizing data and features,” Soft computing journal, vol. 8, no. 1, pp. 70–85, 2021.
[16] H. Firdaus and S. I. Hassan, “Unsupervised learning on healthcare survey data with particle swarm optimization,” in Machine Learning with Health Care Perspective: Machine Learning and Healthcare. Springer, 2020, pp. 57–89.
[17] S. Rahman, M. I. Khan, M. S. Satu, and M. Z. Abedin, “Risk prediction with machine learning in cesarean section: optimizing healthcare operational decisions,” in Signal Processing Techniques for Computational Health Informatics. Springer, 2020, pp. 293–314.
[18] K. Cao, Y. Cui, L. Li, J. Zhou, and S. Hu, “Cpu-gpu cooperative qos optimization of personalized digital healthcare using machine learning and swarm intelligence,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 21, no. 4, pp. 521–533, 2024.
[19] A. Tawhid, T. Teotia, and H. Elmiligi, “Chapter 13 – machine learning for optimizing healthcare resources,” in Machine Learning, Big Data, and IoT for Medical Informatics, ser. Intelligent Data-Centric Systems, P. Kumar, Y. Kumar, and M. A. Tawhid, Eds. Academic Press, 2021, pp. 215–239.
[20] P. Detti, G. Z. M. d. Lara, and M. Benini, “A metaheuristic approach for biological sample transportation in healthcare,” in Graphs and combinatorial optimization: from theory to applications: CTW2020 proceedings. Springer, 2020, pp. 293–306.
[21] S. Dhanasekaran, K. P. M. Kumar, A. S. Kumar, R. Jeya, S. Rajasekaran, B. Murugan, and R. Rajasubramanian, “Chapter 8 - intelligent metaheuristic cluster-based wearable devices for healthcare monitoring in telemedicine systems,” in Wearable Telemedicine Technology for the Healthcare Industry, H. D. Jude, D. Gupta, A. Khanna, and A. Khamparia, Eds. Academic Press, 2022, pp. 109–122.
[22] M. Al-Hashimi, S. Mohammed Jameel, F. Husham Almukhtar, M. M. Abdul Zahra, and R. Adnan Jaleel, “Optimised internet of thing framework based hybrid meta-heuristic algorithms for e-healthcare monitoring,” IET Networks, 2022.
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