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ANESTHESIA DETECTION SYSTEM

AUTHORS:
C Karthick Raja
Mentor
Dr.B.Suresh
Affiliation
Department of Computer Science and Information Technology Institute of Science, Technology and Advanced Studies (VISTAS),Chennai, India
DOI: 10.55041/ijsmt.v2i5.146
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Anesthesia is essential in surgical operations, ensuring that patients remain unconscious and free from pain. A crucial aspect of this procedure is the meticulous management of neuromuscular blockade, which, if not monitored correctly, can pose significant risks. Conventional methods, such as the Train-of-Four (TOF) technique, often rely on subjective manual evaluations that lack uniformity. To overcome these issues, this project utilizes machine learning frameworks to accurately forecast levels of neuromuscular blockade by considering variables such as anesthetic concentration, muscle response, and individual patient characteristics. By implementing a neural network model, patients are classified into specific states: recovery, shallow, moderate, or deep blockade. This advancement provides anesthesiologists with real-time insights, ultimately improving patient safety, reducing human error, and enabling quicker, data-driven clinical decisions. The incorporation of sophisticated predictive analytics in anesthesia practices represents a significant advancement towards more accurate and safer surgical care.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 05
Review Rounds:
2
Article Type:
Research Article
Publication Date:
May 09 2026
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Raja, C. K. (2026). Anesthesia Detection System. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.146

Raja, C. "Anesthesia Detection System." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.146.

Raja, C. "Anesthesia Detection System." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.146.

References
[1]. 1. Parker, M. J., & Thompson, D. (2019). "Machine Learning in Medical Applications." Journal of Healthcare Engineering, 10(2), 34-45. o This paper discusses various machine learning applications in healthcare, including predictive models used in anesthesia management.

[2].2. Kumar, S., & Gupta, R. (2017). "Predicting Neuromuscular Blockage Risk: A Machine Learning Approach." Journal of Anesthesia, 61(3), 501-509. o This study provides a comprehensive analysis of using machine learning algorithms for predicting neuromuscular blockage in patients undergoing anesthesia.

[3]. Zhang, Y., & Liu, W. (2018). "Predictive Analytics in Anesthesia: Using Data for Safer Outcomes." British Journal of Anesthesia, 121(1), 15-22. o This research focuses on how predictive analytics can enhance patient safety by monitoring and forecasting risks associated with anesthesia.

[4].. Hussain, M., & Joshi, S. (2020). "Application of Deep Learning in Healthcare: A Case Study on Neuromuscular Monitoring." Journal of Clinical Medicine, 9(8), 2504.

[5]1. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. o A foundational textbook in machine learning that covers algorithms, data preprocessing, and evaluation techniques used in this project.

  1. 2. Rajasekaran, S., & Pai, G. A. (2003). Neural Networks, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications. Prentice Hall. o This book provides detailed insights into the neural network models and other machine learning techniques employed in the development of the system.

Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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