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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63506
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Applications
BAPUJI INSTITIUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE
Master of Computer Applications
BAPUJI INSTITIUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE
The fact that a Bio-Digital Twin is one approach that will revolutionize personalized healthcare and help model complex biological systems. A Bio Digital Twin is fundamentally a high-fidelity digital twin of a human body, an organ, or even a specific physiological process is one that is continuously and dynamically updated with real-world, real-time data from wearable sensors, electronic medical records, and advanced imaging. Coupling those streams with powerful knowledge bases and Machine Learning, one enabling the digital twin to simulate and predict the physiological and pathological behavior of its physical counterpart. under many conditions. The chief detached of the project is to grow and use a Bio-Digital Twin specifically for predictive analytics. This would enable the forecasting of future health outcomes and facilitate the detection of potential diseases treatment optimization and personalization. Using this system requires the systematic analysis of complex biological signals, genetic information, and longitudinal patient history to identify subtle, anomalous patterns that are often precursors to serious health events. This is the basis for the critical window made available for timely and efficient preventive intervention. This new technique has the ability to provide researchers and professionals the means by which they can non-invasively assess new drugs or treatment methodologies while keeping track of the status of their patients remotely and continuously, and create customized treatment programs for the same, all without subjecting the patients to the risks involved in physical experiments. It gives the meaning of the numerical data through a user-friendly interface. Ultimately, this Bio-Digital is what will revolutionize the present healthcare environment from being merely reactive and symptom-based to being proactive and highly personalized in approach.
KEYWORDS
In recent years, advancements in healthcare technology and artificial intelligence have led to the emergence of innovative concepts such as the Bio-Digital Twin. A bio-digital twin is a virtual representation of a human body or biological system that continuously collects and analyses real-time data from various sources such answerable devices, medical records, and sensors. The main objective of this project, Bio-Digital Twin for Predictive Analysis, is to develop a system that can monitor, simulate, and predict an individual’s health condition. By integrating technologies like machine learning, data analytics, and cloud computing, the system creates a dynamic digital model that mirrors the physical state of a person. This project focuses on : • Collecting and processing real-time health data • Creating a virtual twin of the patient • Applying predictive analytics to forecast potential diseases • Supporting early diagnosis and preventive healthcare The importance of this system lies in its ability to shift traditional healthcare from a reactive approach to a predictive and personalized approach. Instead of treating diseases after they occur, this model helps in identifying risks beforehand, enabling timely intervention Overall, the bio-digital twin concept represents a significant step toward the future of smart healthcare systems, where continuous monitoring and intelligent analysis can improve patient outcomes and enhance the quality of life.
1. Laaki, H, Miche,Y, & Tammi, K. (2021). Proactive Health Monitoring with Digital Twins. In 2021, IEEE 22nd International Conference on Industrial Technology (ICIT) (Vol. 1, pp. 696- 701). IEEE. 2. Bruynseels, K., de Groot, F. H., & van de Stolpe, A. (2018). Digital Twins in Health Care: The Next Step in Personalised Medicine? Journal of Personalised Medicine, 8(3), 32. 3. Elayan, H., Aloqaily, M., & Guizani, M. (2021). Digital Twin for Healthcare: A Survey. IEEE Access, 9, 39604- 39622. 4. Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L, Wang, F., ... & Tao, F. (2019). A Novel Cloud-Based Framework for the Elderly Healthcare Service Using Digital Twin. IEEE Access, 7, 107383-107394. 5. Croatti, A., Gabbiadini, R., Gnesi, S., & Ricci, A. (2020). On the Role of Digital Twins in Healthcare: A Scoping Review. Journal of Medical Systems, 44(8), 1-13.
Publication Date
5/19/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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