AI-BASED HEALTH MONITORING FOR PNEUMONIA PATIENTS

Authors

  • Qobilov Sirojiddin Sherqulovich Teacher, Tashkent University of Information Technologies named after Muhammad ibn Musa al-Khwarizmi, Tashkent, Uzbekistan
  • Elyor Ismoilov Khayrulla ugli Student, Tashkent University of Information Technologies named after Muhammad ibn Musa al-Khwarizmi, Tashkent, Uzbekistan
  • Yorkinjon Abdukhalilov Abdurasul ugli Student, Tashkent University of Information Technologies named after Muhammad ibn Musa al-Khwarizmi, Tashkent, Uzbekistan
  • Sukhrobjon Abdullaev Hayitmurod ugli Student, Tashkent University of Information Technologies named after Muhammad ibn Musa al-Khwarizmi, Tashkent, Uzbekistan
  • Sardor Ibodov Gulmurodovich Student, Tashkent University of Information Technologies named after Muhammad ibn Musa al-Khwarizmi, Tashkent, Uzbekistan

Keywords:

AI-based health monitoring, pneumonia, machine learning algorithms, real-time data analysis, patient outcomes, predictive analytics, data security, regulatory compliance, healthcare.

Abstract

Advancements in artificial intelligence (AI) have revolutionized various industries, and healthcare is no exception. In this article, we explore the application of AI-based health monitoring systems for pneumonia patients. Pneumonia remains a significant global health concern, and early detection and continuous monitoring are crucial for effective management and improved patient outcomes. The proposed AI-based health monitoring system utilizes state-of-the-art machine learning algorithms and sensor technologies to continuously collect and analyze vital health data, enabling healthcare providers to promptly identify deteriorations in a patient's condition and intervene accordingly.

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References

S. M. R. Islam, D. Kwak, M. D. H. Kabir, M. Hossain, K. S. Kwak, The internet of things for health care: a comprehensive survey, IEEE Acces, 3 (2015), 678–708. https://doi.org/10.1109/ACCESS.2015.2437951

H. Zaynidinov, J. Nurmurodov, S. Qobilov, Application of Machine Learning Methods for Signal Processing in Piecewise-Polynomial Bases, 2023 IX International Conference on Information …, 2023 https://ieeexplore.ieee.org/abstract/document/10139002/

A. Rahaman, M. M. Islam, M. R. Islam, M. S. Sadi, S. Nooruddin, Developing IoT based smart health monitoring systems: a review, Rev. Intell. Artif., 33 (2019), 435–440. https://doi.org/10.18280/ria.330605

S. Bakhromov, J. Jumaev, S. Kobilov, M. Tukhtasinov, Analysis of the construction of local interpolation cubic splines on the basis of detailed data, AIP Conference Proceedings, 2023. https://pubs.aip.org/aip/acp/article/2781/1/020077/2895332

G. Mois, S. Folea, T. Sanislav, Analysis of three IoT-based wireless sensors for environmental monitoring, IEEE Trans. Instrum. Meas., 66 (2017), 2056–2064. https://doi.org/10.1109/TIM.2017.2677619

M. Hasan, M. M. Islam, M. I. I. Zarif, M. M. A. Hashem, Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches, Internet Things, 7 (2019), 100059. https://doi.org/10.1016/j.iot.2019.100059

M. Islam, N. Neom, M. Imtiaz, S. Nooruddin, M. Islam, M. Islam, A review on fall detection systems using data from smartphone sensors, Ingénierie des systèmes d Inf., 24 (2019), 569–576. https://doi.org/10.18280/isi.240602

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Published

2023-07-31

How to Cite

AI-BASED HEALTH MONITORING FOR PNEUMONIA PATIENTS. (2023). International Bulletin of Applied Science and Technology, 3(7), 390-393. https://researchcitations.com/index.php/ibast/article/view/2405

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