AI-Based Adaptive Genome Digital Twin for Personalized Health Prediction

Abstract

Doctors usually get involved after a patient already feels ill. That is a poor fit for chronic disease, since the damage builds up quietly for years before anyone notices. We built a genome digital twin to deal with this. It is a virtual copy of one person, started from their inherited disease risk and updated with their vital signs, sleep, diet and activity. The inherited part is a polygenic risk score and it never changes. Everything else arrives as new readings and is merged into the twin each time. Random Forest, XGBoost and a Decision Tree baseline take the combined record and give the probability of chronic disease. Results go to a Streamlit dashboard that shows a risk percentage, a biological age, a health score and a wellness index. It also splits the risk into genetic, lifestyle, environmental and clinical shares, and a user can change a habit to watch the forecast move. The paper describes the requirements, a four-layer architecture, how the twin is kept current, the training and evaluation plan, the tests and a twelve-week schedule. We also discuss the limits of the work and the care that genomic data demands. What we think is new is simple: the genetic baseline, the live readings and the what-if simulation sit inside one model, not three separate tools.

Introduction

The text presents an AI-based adaptive genome digital twin for predicting and preventing chronic diseases. Traditional healthcare often identifies conditions such as hypertension, type 2 diabetes, and coronary artery disease only after symptoms appear. The proposed system aims to predict disease risks earlier by combining genetic information with continuously updated physiological, lifestyle, and clinical data.

A digital twin is a virtual representation of an individual that evolves as new health information becomes available. Unlike traditional genetic reports or wearable applications, which provide only partial health information, the proposed system integrates multiple data sources into a unified model to estimate personalized chronic disease risk.

1. Background and Key Concepts

The system uses four major types of health data:

• Genomic data: A Polygenic Risk Score (PRS) summarizes the inherited susceptibility to common diseases based on multiple genetic variants. It indicates risk rather than providing a diagnosis.

• Physiological data: Wearable devices provide measurements such as heart rate, blood pressure, blood oxygen levels, and sleep patterns.

• Lifestyle data: Information about diet, physical activity, and daily habits helps assess modifiable risk factors.

• Clinical data: Age, gender, body mass index, and medical history provide additional health information.

The system also estimates biological age, an indicator intended to reflect the body's health-related condition compared with chronological age.

2. Research Gap

Existing healthcare technologies have several limitations. Routine clinical care is often reactive, genetic testing generally provides static reports, and wearable devices monitor current physiological conditions without considering genetic susceptibility. Many machine-learning models also rely on only one type of data.

The research addresses these limitations by proposing a continuously updated digital twin that integrates genetic predisposition with changing health measurements and lifestyle behaviors. It also allows users to explore hypothetical lifestyle changes and examine their potential effects on predicted health risks.

3. Proposed System and Main Contributions

The proposed system combines genetic markers, wearable readings, lifestyle information, and clinical records into a unified feature set. Machine-learning models then estimate chronic disease risk, with predictions updated as new observations become available.

Its main contributions include:

• Integrated health modeling: Combines genetic and real-time health data in a single predictive framework.

• Adaptive updates: Incorporates new observations using a smoothing mechanism, helping the model respond to genuine changes without overreacting to unusual readings.

• Risk decomposition: Separates estimated risk into genetic, lifestyle, environmental, and clinical components to help users understand contributing factors.

• What-if simulations: Allows users to explore how changes in habits might affect predicted disease risk and biological age.

• Open-source prototype: Uses freely available tools and a synthetic Polygenic Risk Score to support initial experimentation without requiring actual genetic data.

4. Existing Systems Compared with the Proposed Approach

5. Literature Review and Challenges

Previous research has demonstrated the potential of precision medicine, artificial intelligence, polygenic risk scores, and digital twins for personalized healthcare. However, the literature identifies gaps in combining genetic and lifestyle information, continuously updating individual risk estimates, and providing interactive simulations of behavioral changes.

The text also emphasizes important limitations. Polygenic risk scores may perform less accurately for populations underrepresented in genetic research, potentially increasing health inequalities. Machine-learning predictions must therefore be carefully validated, interpreted cautiously, and presented as probabilities rather than definitive diagnoses.

Conclusion

This paper presented an AI-based adaptive genome digital twin. It joins inherited risk with live physiological and lifestyle data to forecast chronic disease for one person. Four ideas support the design: a fixed genomic baseline, a smoothed dynamic state that follows the person, ensemble learners that relate the two, and a what-if interface that links prediction to action. With them, health management can move from reaction toward prevention, and patients and clinicians share one picture of likely futures. Several next steps are clear. The most important is to replace the synthetic score with real genotype data and check the models against clinical outcomes across different ancestries. Live wearable feeds could replace manual entry. The model could cover more disease categories and use time-series learners that exploit the full history of readings. Feature-attribution methods would make each prediction easier to explain, and federated or on-device learning could keep sensitive data in the user\'s hands. Prospective studies with clinicians would then show whether seeing a personal twin changes behavior and outcomes.

References

[1] G. S. Ginsburg and K. A. Phillips, “Precision medicine: From science to value,” Health Aff., vol. 37, no. 5, pp. 694–701, 2018. [2] E. J. Topol, “High-performance medicine: The convergence of human and artificial intelligence,” Nat. Med., vol. 25, no. 1, pp. 44–56, 2019. [3] M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems,” in Transdisciplinary Perspectives on Complex Systems, F.-J. Kahlen, S. Flumerfelt, and A. Alves, Eds. Cham, Switzerland: Springer, 2017, pp. 85–113. [4] B. Björnsson et al., “Digital twins to personalize medicine,” Genome Med., vol. 12, Art. no. 4, 2020. [5] J. Corral-Acero et al., “The ‘digital twin’ to enable the vision of precision cardiology,” Eur. Heart J., vol. 41, no. 48, pp. 4556–4564, 2020. [6] A. V. Khera et al., “Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations,” Nat. Genet., vol. 50, no. 9, pp. 1219–1224, 2018. [7] Y. Hasin, M. Seldin, and A. Lusis, “Multi-omics approaches to disease,” Genome Biol., vol. 18, Art. no. 83, 2017. [8] S. Horvath, “DNA methylation age of human tissues and cell types,” Genome Biol., vol. 14, Art. no. R115, 2013. [9] A. Rajkomar, J. Dean, and I. Kohane, “Machine learning in medicine,” N. Engl. J. Med., vol. 380, no. 14, pp. 1347–1358, 2019. [10] A. Torkamani, N. E. Wineinger, and E. J. Topol, “The personal and clinical utility of polygenic risk scores,” Nat. Rev. Genet., vol. 19, no. 9, pp. 581–590, 2018. [11] A. R. Martin et al., “Clinical use of current polygenic risk scores may exacerbate health disparities,” Nat. Genet., vol. 51, no. 4, pp. 584–591, 2019. [12] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Belmont, CA, USA: Wadsworth, 1984. [13] L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001. [14] J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Ann. Statist., vol. 29, no. 5, pp. 1189–1232, 2001. [15] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, New York, NY, USA, 2016, pp. 785–794. [16] F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. [17] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 30, 2017, pp. 4765–4774.

Copyright

Copyright © 2026 Dr. S. Senthil Kumar, Rajeshwarar M, Mohamed Shahil M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.