Improving data processing in medical education through machine learning

The exponential growth of biomedical data coupled with advances in machine learning (ML) has created opportunities for more precise diagnosis, enhanced treatment planning, and improved patient management. However, the successful implementation of ML in clinical settings depends on healthcare professionals’ understanding and competency in these technologies. This study examines the effectiveness of integrating ML methodologies into the curricula of Astana Medical University and S. D. Asfendiyarov Kazakh National Medical University. Focusing on childhood allergic conditions such as asthma, rhinitis, and skin diseases, a supervised ML approach (linear regression) was employed to analyze both clinical and educational data. Results showed that the experimental group of students who received ML-integrated training demonstrated significant improvements in analytical competence and data processing accuracy compared to the control group. The ML model achieved a coefficient of determination (R2) of 0,85 with low prediction errors (MAE=0,45, MSE=0,30, RMSE=0,55). Statistical tests supported the hypothesis that structured ML education enhances medical students’ competencies, suggesting that future healthcare professionals trained in ML can better leverage data-driven decision-­making for improved patient care. This study contributes to the growing body of literature advocating for ML integration in medical education and underscores the need for further research into advanced ML algorithms and long-term clinical outcomes.

1. Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2007;2:59-77.

2. Jia Z, Chen J, Xu X, et al. The importance of resource awareness in artificial intelligence for healthcare. Nat Mach Intell. 2023; 5(7):687-98.

3. Cunningham P, Cord M, Delany SJ. Supervised Learning. In: Cord M, Cunningham P, editors. Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval [In­ternet]. Berlin, Heidelberg: Springer; 2008 p. 21-49. doi:10.1007/978-3-540-75171-7_2.

4. Naik N, Rallapalli Y, Krishna M, et al. Demystifying the Ad­van­cements of Big Data Analytics in Medical Diagnosis: An Overview. Eng Sci. 2021;19(2):42-58.

5. Scott I, Carter S, Coiera E. Clinician checklist for assessing sui­ta­bility of machine learning applications in healthcare: BMJ Health & Care Informatics 2021;28:e100251. doi:10.1136/bmjhci-2020-100251.

6. Orynbaeva AS, Shindaliyev NT, Abdikadyr ZN. Possibilities of Using Machine Learning Algorithms in Medical Data Processing: Manual for Students. Astana Aktaulova’s LLP; 2024. 190 p.

7. Gui C, Chan V. Machine learning in medicine. Univ West Ont Med J. 2017;86(2):76-8.

8. Habehh H, Gohel S. Machine Learning in Healthcare. Curr Geno­mics. 2021;22(4):291-300.

9. Dhillon A, Singh A. Machine learning in healthcare data analysis: a survey. J Biol Today’s World. 2019;8(6):1-10.

10. Ghassemi M, Naumann T, Schulam P, et al. A Review of Chal­lenges and Opportunities in Machine Learning for Health. AMIA Summits Transl Sci Proc. 2020;2020:191-200.

11. Nayyar A, Gadhavi L, Zaman N. Chapter 2 — Machine learning in healthcare: review, opportunities and challenges. In: Machine learning in healthcare: review, opportunities and challenges. 2021;23-45. doi:10.1016/B978-0-12-821229-5.00011-2.

12. Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127-57.

13. Magoulas GD, Prentza A. Machine learning in medical appli­cations. In: Advanced course on artificial intelligence. Springer; 1999. p. 300-7.

14. Sendak MP, D’Arcy J, Kashyap S, et al. A Path for Translation of Machine Learning Products into Healthcare Delivery. EMJ Innov. 2020. doi:10.33590/emjinnov/19-00172.

15. Deo RC. Machine Learning in Medicine. Circulation. 2015;132(20): 1920-30.

16. Zamzam AH, Abdul Wahab AK, Azizan MM, et al. A Systematic Re­view of Medical Equipment Reliability Assessment in Im­pro­ving the Quality of Healthcare Services. Front Public Health. 2021;9:753951.

17. Cajal B, Jiménez R, Gervilla E, Montaño JJ. Doing a Systematic Review in Health Sciences. Clin Health. 2020;31(2):77-83.

18. Goodacre R, Broadhurst D, Smilde AK, et al. Proposed minimum reporting standards for data analysis in metabolomics. Meta­bolomics. 2007;3(3):231-41.

19. Montgomery DC, Peck EA, Vining GG. Introduction to Linear Regression Analysis. 5th edition. Hoboken, NJ: John Wiley & Sons Inc; 2012. 645 p.

20. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019;380(14):1347-58.

21. van Breugel M, Fehrmann RSN, Bügel M, et al. Current state and prospects of artificial intelligence in allergy. Allergy. 2023; 78(10):2623-43.

22. Khan M, Banerjee S, Muskawad S, et al. The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment. Curr Allergy Asthma Rep. 2024;24(7):361-72.

23. Breiteneder H, Diamant Z, Eiwegger T, et al. Future research trends in understanding the mechanisms underlying allergic diseases for improved patient care. Allergy. 2019;74(12):2293-311.

24. Rabe KF, Adachi M, Lai CKW, et al. Worldwide severity and control of asthma in children and adults: The global asthma insights and reality surveys. J Allergy Clin Immunol. 2004;114(1):40-7.