Automation of predicting the risk of osteoporosis

Purpose of research. To increase the accuracy of forecasting by developing optimal predictive models that increase the reliability of the identified estimates in the diagnosis of osteoporosis using an expert system.

Methods. In this work, parametric statistical methods were used to confirm the validity of the assumption of the normal distribution of all studied sample populations. Based on the results obtained, correlation and regression analyses are considered as the best approach for studying interrelations, for developing predictive models with determining the confidence interval of prediction and comparing the range of the final indicator with the probability of disease occurrence.

Result. During the study, predictive models were constructed for each identified patient group along with limits of confidence intervals for predicted values of key parameters. Regularities in the mutual dependence between specific target factor ranges and degrees of pathology progression probabilities have been established. An expert information system has been developed, implementing a complete informational support system designed to identify risks of diseases development in patients based on provided data regarding chest tissue density.

Conclusion. The developed information system enables predicting the likelihood of illness for patients (both men and women) whose age exceeds the specified range (10–70 years); however, the reliability of such predictions remains ambiguous since the prognostic models underlying the system's operation are built upon data from individuals aged within the period of approximately 10 to 72 years.

1. Golinelli D. Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literatur. J Med Internet Res. 2020; 22(11): 12020. DOI: 10.2196/22280..

2. Gubbins S., Richardson J., Baylis M., Wilson A.J., Abrahantes J.C. Modelling the continental-scale spread of Schmallenberg virus in Europe: approaches and challenges. Prev Vet Med. 2014; 116(4):404-11. https://doi.org/10.1016/j.prevetmed.2014.02.004.

3. Ivashchuk O.D., et al. Intelligent system for assessing the quality of ore. Iskusstvennyi intellekt i prinyatie reshenii = Artificial intelligence and decision making. 2023; 4: 94-102 (In Russ.).

4. Zwiener I., Blettner M., Hommel G. Survival analysis: part 15 of a series on evaluation of scientific publications. Dtsch Arztebl Int. Germany. Cologne: Deutscher ÄrzteVerlag. 2011; 108(10):163-9. https://doi.org/10.3238/arztebl.2010.0163.

5. Altman D. G., Machin D., Bryant T. N., Gardner M.J. Statistics with Confidence: Confidence Intervals and Statistical Guidelines. London: British Medical Journal Publications; 2000. P. 240. doi: 10.1007/s10654-016-0149-3.

6. Prel J.-B., Hommel G., Röhrig B., Blettner M. onfidence Interval or PValue Part 4 of a Series on Evaluation of Scientific Publications. Deutsches Ärzteblatt International. 2009; 106 (19): 335–339. https://doi.org/10.3238/arztebl.2009.0335

7. Grjibovski A.M., Ivanov S.V., Gorbatova M.A. Univariate regression analysis using statistica and spss software. Nauka i Zdravookhranenie = Science & Healthcare Semey. 2017; (2): 5-33. (In Russ.). https://doi.org/10.34689/SH.2017.19.2.001.]

8. Bewick V., Cheek L., Ball J. Statistics review 14: Logistic regression. Crit Care. 2005; 9 (1): 112–118. https://doi.org/10.1186/cc3045

9. Erokhin S.D., Borisenko B.B., Martishin L.D., Fadeev A.S. Analysis of existing methods to reduce the dimensionality of input data. T-Comm: Telekommunikatsii i transport = T-Comm. 2022; 16 (1): 30-35. (In Russ.). https://doi.org/10.36724/2072-8735-2022-16-1-30-37.

10. Grjibovski A. M., Ivanov S. V., Gorbatova M. A. Analysis of quantitative data in two independent samples using Statistica and SPSS software: parametric and non-parametric tests. Nauka i Zdravookhranenie = Science & Healthcare. 2016; 2: 5-28. (In Russ.).

11. Bazilevskiy M.P. Multi-criteria approach to pair-multiple linear regression models constructing. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics. 2021; 1: 88-99 (In Russ.).

12. Karel G.M. Moons, Douglas G. Altman, Johannes B. Reitsma, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Ann Intern Med. 2015;162:W1-W73. https://doi.org/10.7326/M14-0698.

13. Bykova V.V., Kataeva A.V. Methods and tools for analysing informative features when processing medical data. Programmnye produkty i sistemy = Software & Systems. 2016; (2):172-178. (In Russ.).

14. Ajana S., Acar N., Bretillon L., et al. Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size. Bioinformatics. London: Oxford Academic. 2019; 35(19):3628–34. https://doi.org/10.1093/bioinformatics/btz135

15. Yalaev B.I., Novikov A.V., Minniakhmetov I.R., Khusainova R.I. Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training. Problemy endokrinologii = Problems of Endocrinology. 2024; 70(6). (In Russ.). https://doi.org/10.14341/probl13421

16. Kiselev A.V., Petrova T.V., Degtyaryov S.V., Rybochkin A.F., Filist S.A., Shatalova O.V., Mishustin V. N. Hybrid Deciding Modules with Virtual Streams for Classification and Prediction of Functional State of Complex Systems. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University. 2018; 22(4): 123-134 (In Russ.). https://doi.org/10.21869/2223-1560-2018-22-4-123-134.

17. Lipatova A.V., Potapchenko T.D. Razrabotka analiticheskoj sistemy ocenki vozniknoveniya riskov zdorov'yu naseleniya na baze algoritmov mashinnogo obucheniya. Iskusstvennyj intellekt i mashinnoe obuchenie. Cifra. Komp'yuternye nauki i informatika = Cifra. Komp'yuternye nauki i informatika. 2025; (1) (In Russ.). https://doi.org/10.60797/COMP.2025.5.3.

18. Ivashchuk O.D., Nesterova E.V., Igrunova S.V., Kaliuzhnaya E.V., Udovenko I.V. Forecasting the environmental situation at the purification plants of the enterprise based on fuzzy logic. Journal of Physics: Conference Series, IV International Conference on Applied Physics, Information Technologies and Engineering (APITECH-IV 2022). 2022; 2388. DOI: 10.1088/1742-6596/2388/1/012039.

19. Ivashchuk O.D., et al. Prognozirovanie izmeneniya proizvoditel'nosti mel'nic obogatitel'noj fabriki pri izmenenii granulometricheskogo sostava pitayushchej rudy. Sovremennye naukoemkie tekhnologii. 2023; (11): 33-38 (In Russ.). https://doi.org/10.17513/snt.3981. URL: https://top-technologies.ru/ru/article/view?id=39817. 7

20. Ivashchuk O. A., et al. Razrabotka modelej prognozirovaniya effektivnosti raboty valkovoj drobilki vysokogo davleniya na osnove regressionnogo analiza. STIN. 2020; (6): 37-40 (In Russ.).

21. Muns KGM, et al. Transparent reporting on a multifactorial predictive model for individual forecasting or diagnosis (TRIPOD): explanation and clarification. Cifrovaya diagnostika. 2022; 3.3: 232-322. (In Russ.). https://doi.org/10.15690/vsp.v22i2.2557