An Efficient Hybrid Model for Healthcare System to Detect‎Disease Using Machine Learning Techniques

  • Authors

    • Ajay Singh Mavai PhD Scholar, Amity University Madhya Pradesh, Gwalior, M.P. India
    • Devendra Kumar Mishra Amity University Madhya Pradesh, Gwalior, M.P. India
    • Abhishek Kumar Sharma The LNM Institute of Information Technology, Jaipur, Rajasthan, India
    https://doi.org/10.14419/mq9yvg65

    Received date: August 25, 2025

    Accepted date: November 4, 2025

    Published date: December 12, 2025

  • Healthcare; Machine Learning; Personalized Medicine; Predictive Analytics; Prognosis and ‎Disease Prediction

  • Abstract

    Over the past decade, the huge demand for personalized and proactive healthcare has led to the ‎integration of advanced technologies such as wearable devices, IoT and AI into modern ‎healthcare. The project proposes the design of a smart health care system specifically for elderly ‎people and disabled persons, which continuously monitors their health and provides early disease ‎prediction. It proposes integrating a system of patient medical records with real-time ‎physiological data collected by multiple IoT-based wearable devices, while ensuring reliable and ‎timely assistance. IoT-based networks work on standardized communication protocols to ‎promote and manage data efficiency. In addition, it uses sophisticated machine learning ‎algorithms to evaluate collected data, identify potential health risks and support clinical decision-‎making with utmost accuracy and precision. It enhances the overall effectiveness and scalability ‎of healthcare service in addition to improving early treatment outcomes. Thus, by addressing ‎issues of heterogeneity, missing values, and interpretability, this proposed strategy advances the ‎creation of an inclusive healthcare system‎.

  • References

    1. World Health Organization. (2021). Decade of healthy ageing 2020–2030. https://www.who.int/initiatives/decade-of-healthy-ageing
    2. An, Q., Rahman, S., Zhou, J., & Kang, J. J. (2023). A comprehensive review on machine learning in healthcare industry: Classification, restrictions, opportunities and challenges. Sensors, 23(9), 4178. https://doi.org/10.3390/s23094178.
    3. Iyortsuun, N. K., Kim, S.-H., Jhon, M., Yang, H.-J., & Pant, S. (2023). A review of machine learning and deep learning approaches on mental health diagnosis. Healthcare, 11(3), 285. https://doi.org/10.3390/healthcare11030285.
    4. Preti, L. M., Ardito, V., Compagni, A., Petracca, F., & Cappellaro, G. (2024). Implementation of machine learning applications in health care organ-izations: Systematic review of empirical studies. Journal of Medical Internet Research, 26, e55897. https://doi.org/10.2196/55897.
    5. Jeyaraman, N., Jeyaraman, M., Yadav, S., Ramasubramanian, S., Balaji, S., Muthu, S., Lekha, P. C., & Patro, B. P. (2024). Applications of fog computing in healthcare. Cureus. https://doi.org/10.7759/cureus.64263.
    6. Mir, A. A., Khalid, A. S., Musa, S., Ahmad Fauzi, M. F., Abdul Razak, N. N., & Tang, T. B. (2025). Machine learning in ambient assisted living for enhanced elderly healthcare: A systematic literature review. IEEE Access, 13, 110508–110527. https://doi.org/10.1109/ACCESS.2025.3580961.
    7. Cheema, B., Hourmozdi, J., Kline, A., Ahmad, F., & Khera, R. (2025). Artificial intelligence in the management of heart failure. Journal of Cardiac Failure. https://doi.org/10.1016/j.cardfail.2025.02.020.
    8. Hummelsberger, P., Koch, T. K., Rauh, S., Dorn, J., Lermer, E., Raue, M., ... & Gaube, S. (2023). Insights on the current state and future outlook of AI in health care: Expert interview study. JMIR AI, 2, e47353. https://doi.org/10.2196/47353.
    9. Poddar, M., Marwaha, J. S., Yuan, W., et al. (2024). An operational guide to translational clinical machine learning in academic medical centers. NPJ Digital Medicine, 7, 129. https://doi.org/10.1038/s41746-024-01094-9.
    10. Schmidt, J., Schutte, N. M., Buttigieg, S., et al. (2024). Mapping the regulatory landscape for artificial intelligence in health within the European Union. NPJ Digital Medicine, 7, 229. https://doi.org/10.1038/s41746-024-01221-6.
    11. Liu, G. S., Fereydooni, S., Lee, M. C., et al. (2025). Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryn-gology-head and neck surgery. NPJ Digital Medicine, 8, 265. https://doi.org/10.1038/s41746-025-01693-0.
    12. Rehman, M. U., Naseem, S., Butt, A., et al. (2025). Predicting coronary heart disease with advanced machine learning classifiers for improved car-diovascular risk assessment. Scientific Reports, 15, 13361. https://doi.org/10.1038/s41598-025-96437-1.
    13. Li, X., Shang, C., Xu, C., et al. (2023). Development and comparison of machine learning-based models for predicting heart failure after acute my-ocardial infarction. BMC Medical Informatics and Decision Making, 23, 165. https://doi.org/10.1186/s12911-023-02240-1.
    14. Sarraf, S., & Tofighi, G. (2016). Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data. Future Generation Computer Sys-tems, 106, 526–535. https://doi.org/10.1016/j.future.2016.10.021.
    15. Gupta, R. S., Wood, C. E., Engstrom, T., et al. (2025). A systematic review of quantum machine learning for digital health. NPJ Digital Medicine, 8, 237. https://doi.org/10.1038/s41746-025-01597-z.
    16. Garg, R. (2025). Smart aging: Harnessing artificial intelligence to enhance elderly health care and independence. Journal of the Indian Academy of Geriatrics, 21(2), 143–146. https://doi.org/10.4103/jiag.jiag_67_24.
    17. López, L. J. L., Elsharief, S., Jorf, D. A., Darwish, F., Ma, C., & Shamout, F. E. (2025). Uncertainty quantification for machine learning in healthcare: A survey (Version 1). arXiv.
    18. Chustecki, M. (2024). Benefits and risks of AI in health care: Narrative review. Interactive Journal of Medical Research, 13, e53616. https://doi.org/10.2196/53616.
    19. Ashfaq, M. T., Javaid, N., Alrajeh, N., & Ali, S. S. (2025). An explainable AI–based deep learning solution for efficient heart disease prediction at early stages. Evolving Systems, 16, 33. https://doi.org/10.1007/s12530-025-09664-2.
    20. Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., ... & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340. https://doi.org/10.1038/s41591-019-0548-6.
    21. Taha, K. (2025). Machine learning in biomedical and health big data: A comprehensive survey with empirical and experimental insights. Journal of Big Data, 12, 61. https://doi.org/10.1186/s40537-025-01108-7.
    22. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., ... & Ng, A. Y. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225. https://arxiv.org/abs/1711.05225.
    23. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119. https://doi.org/10.1038/s41746-020-00323-1.
    24. Noeyislearning. (n.d.). Framingham heart study [Data set]. Kaggle. https://www.kaggle.com/datasets/noeyislearning/framingham-heart-study
    25. Habibzadeh, H., Dinesh, K., Shishvan, O. R., Boggio-Dandry, A., Sharma, G., & Soyata, T. (2019). A survey of healthcare Internet-of-Things (HI-oT): A clinical perspective. IEEE Internet of Things Journal, 6(6), 1–1. https://doi.org/10.1109/JIOT.2019.2946359.
    26. Teo, Z. L., Jin, L., Li, S., Miao, D., Zhang, X., Ng, W. Y., ... & Ting, D. S. W. (2024). Federated machine learning in healthcare: A systematic re-view on clinical applications and technical architecture. Cell Reports Medicine, 5(2), 101419. https://doi.org/10.1016/j.xcrm.2024.101419.
    27. Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., ... & Luo, Y. (2022). Multimodal machine learning in precision health: A scoping re-view. NPJ Digital Medicine, 5, 171. https://doi.org/10.1038/s41746-022-00712-8.
    28. Bienefeld, N., Boss, J. M., Lüthy, R., Brodbeck, D., Azzati, J., Blaser, M., ... & Keller, E. (2023). Solving the explainable AI conundrum by bridg-ing clinicians’ needs and developers’ goals. NPJ Digital Medicine, 6, 94. https://doi.org/10.1038/s41746-023-00837-4.
    29. Alkhanbouli, R., Almadhaani, H. M. A., Alhosani, F., Simsekler, M. C. E., et al. (2025). The role of explainable artificial intelligence in disease pre-diction: A systematic literature review and future research directions. BMC Medical Informatics and Decision Making, 25, 110. https://doi.org/10.1186/s12911-025-02944-6.
    30. Teoh, J. R., Dong, J., Zuo, X., Lai, K. W., Hasikin, K., & Wu, X. (2024). Advancing healthcare through multimodal data fusion: A comprehensive review of techniques and applications. PeerJ Computer Science, 10, e2298. https://doi.org/10.7717/peerj-cs.2298.
    31. Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022). Multimodal biomedical AI. Nature Medicine, 28, 1773–1784. https://doi.org/10.1038/s41591-022-01981-2.
    32. Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443. https://doi.org/10.1109/TPAMI.2018.2798607.
    33. Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2022). Multimodal deep learning in healthcare: A review, taxonomy, and future directions. NPJ Digital Medicine, 5, 1–24. https://doi.org/10.1038/s41746-022-00665-y.
    34. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342.
    35. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
    36. Lee, J., Yoon, W., & Kim, J. (2020). Cross-institutional validation of deep learning models in healthcare: Challenges and strategies. Journal of Bio-medical Informatics, 109, 103519. https://doi.org/10.1016/j.jbi.2020.103519.
    37. Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intel-ligence. BMC Medicine, 17(1), 1–9. https://doi.org/10.1186/s12916-019-1426-2.
    38. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artifi-cial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953.
    39. Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intel-ligence. BMC Medicine, 17(1), 1–9. https://doi.org/10.1186/s12916-019-1426-2.
    40. Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2022). Multimodal deep learning in healthcare: A review, taxonomy, and future directions. NPJ Digital Medicine, 5(1), 1–24. https://doi.org/10.1038/s41746-022-00665-y.
    41. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749.
    42. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3287560.
    43. Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689.

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    Mavai, A. S., Mishra, D. K. ., & Sharma, A. K. (2025). An Efficient Hybrid Model for Healthcare System to Detect‎Disease Using Machine Learning Techniques. International Journal of Basic and Applied Sciences, 14(8), 240-250. https://doi.org/10.14419/mq9yvg65