Keywords: bio-age, biological age, aging mechanisms, web-application for determining bio-age, machine learning in medicine
Development of a web-application to predict biological age by functional indicators
UDC 51-76
DOI: 10.26102/2310-6018/2022.37.2.015
The rate of aging is a complex indicator of human health which depends on many factors that include external and internal effects on the body (disease and its correction processes), which is reflected in the biomedical indicators of the body (functional, biochemical, hematological and others). To determine the rate of aging, the concept of bio-age is widely used, which is a complex parameter based on ascertaining the degree of human body aging (wear, damage) in reliance on its biomedical parameters. This article presents the development of a client-server web-application for determining the bio-age of a user by evaluating their functional indicators - systolic blood pressure, diastolic blood pressure, breathing delay time on inhalation, breathing delay time on exhalation, the value of lungs vital capacity, hearing acuity, the state of eye lens accommodation, static balancing time, body weight, height. The web-application allows doctors and administrators to determine the patient's bio-age, drawing on the user's functional data entered in the application, taking into account the influence of geroprophylactic therapy. The web-application displays data in the form of a list and a graph and enables one to send reports to the patient's email and to upload them. The server part of the application is written in the C# programming language and ASP.NET framework. The TypeScript programming language and the React framework with the Antd user interface component library were employed to design the client part of the application. PostgresSQL is utilized as a database. As a module for predicting biological age, a previously developed mathematical model, trained on a data sample of 650 records and having an accuracy of 5.87 years, is applied. The ability to predict the patient's bio-age with consideration to the duration and a type of geoprophylactic exposure makes the developed application a suitable tool to identify the leading mechanism of a patient’s aging.
1. Abramovich S.G. Human biological age. Sibirskii meditsinskii zhurnal = Siberian Medical Journal. 1999;4:4–7. (In Russ.)
2. Limanovskaya O.V., Gavrilov I.V., Meshchaninov V.N., Shcherbakov D.L., Kolos E.N. Modeling the biological age of patients based on their functional indicators. Modelirovaniye, optimizatsiya i informatsionnyye tekhnologii = Modeling, Optimization and Information Technology. 2021;9(2):1–16. DOI: 10.26102/2310-6018/2021.33.2.028 (In Russ.)
3. Samorodskaya I.V., Starinskaya M.A. Biological age and the rate of aging as a risk factor for non-communicable diseases and deaths. Profilakticheskaya meditsina. 2016;19(5):41–46. DOI 10.17116/profmed201619541-46. (In Russ.)
4. Wu J.W., Yaqub A., Ma Y. et al. Biological age in healthy elderly predicts aging-related diseases including dementia. Sci Rep. 2021;11:1–10. DOI: 10.1038/s41598-021-95425-5.
5. Pyrkov T.V., Sokolov I.S., Fedichev P.O. Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience. Aging. 2021;13(6):7900–7913. DOI: 10.18632/aging.202816.
6. Humanity. Available by: https://www.humanity.health (accessed on 13.04.2022).
7. Putin E., Mamoshina P., Aliper A., Korzinkin M., Moskalev A., Kolosov A., Ostrovskiy A., Cantor C., Vijg J., Zhavoronkov A. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging. 2016;8(5):1021–1030. DOI: 10.18632/aging.100968.
8. Tanatkanova A. K., Zhambaeva A. K. Building of client-server applications. Nauka i obrazovanie segodnya. 2019;41:6(2). (In Russ.)
9. Khalil M. E., Ghani K., Khalil W. Onion architecture: a new approach for XaaS (every-thing-as-a service) based virtual collaborations. 13th Learning and Technology Conference (L&T). 2016;1:1–7. DOI: 10.1109/LT.2016.7562859.
10. NET documentation. Available at: https://docs.microsoft.com/en-us/dotnet (accessed on 06.04.2022).
11. Stonebraker M., Rowe L.A., Hirohama M. The Implementation of Postgres. IEEE Transactions on Knowledge and Data Engineering. 1990;2(1):340–355. DOI:10.1109/69.50912.
12. Adya A., Blakeley J.A., Melnik S., Muralidhar S. Anatomy of the ADO.NET entity framework. Proceedings of the 2007 ACM SIGMOD international conference on Management of data. 2007;1:877–888. DOI: 10.1145/1247480.1247580.
13. Open Neural Network Exchange. Available by: https://onnx.ai (accessed on 06.04.2022).
14. Suresh M., Hoang H. An Architectural Style for Single Page Scalable Modern Web Application. International Journal of Recent Research Aspects. 2018;5(4):6–13.
15. TypeScript is JavaScript with syntax for types. Available by: https://www.typescriptlang.org (accessed on 06.04.2022).
16. React – A JavaScript library for building user interfaces. Available by: https://reactjs.org (accessed on 06.04.2022).
17. Azure DevOps. Available by: https://azure.microsoft.com/en-us/services/devops (accessed on 06.04.2022).
18. Zhang Y., Wang H., Vasilescu B., Filkov V. One Size Does Not Fit All: An Empirical Study of Containerized Continuous Deployment Workflows. Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2018;1:295–306. DOI: 10.1145/3236024.3236033.
19. Myakotnykh V.S., Meshchaninov V.N., Borovkova T.A., Sidenkova A.P. Teoriya i praktika sovremennoy gerontologii: monografiya. Yekaterinburg: OOO «IITS «Znak kachestva»; 2022. 280 p. (In Russ.)
Keywords: bio-age, biological age, aging mechanisms, web-application for determining bio-age, machine learning in medicine
For citation: Zotov A.O., Limanovskaya O.V., Gavrilov I.V., Meshchaninov V.N. Development of a web-application to predict biological age by functional indicators. Modeling, Optimization and Information Technology. 2022;10(2). URL: https://moitvivt.ru/ru/journal/pdf?id=1177 DOI: 10.26102/2310-6018/2022.37.2.015 (In Russ).
Received 29.04.2022
Revised 23.05.2022
Accepted 31.05.2022
Published 30.06.2022