Preview

Известия высших учебных заведений России. Радиоэлектроника

Расширенный поиск

Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients

https://doi.org/10.32603/1993-8985-2022-25-4-116-122

Аннотация

Introduction. Patients with diabetes are exposed to various cardiovascular risk factors, which lead to an increased risk of cardiac complications. Therefore, the development of a diagnostic system for diabetes and cardiovascular disease (CVD) is a relevant research task. In addition, the identification of the most significant indicators of both diseases may help physicians improve treatment, speed the diagnosis, and decrease its computational costs.

Aim. To classify subjects with different diabetes types, predict the risk of cardiovascular diseases in diabetic patients using machine learning methods by finding the correlational indicators.

Materials and methods. The NHANES database was used following preprocessing and balancing its data. Machine learning methods were used to classify diabetes based on physical examination data and laboratory data. Feature selection methods were used to derive the most significant indicators for predicting CVD risk in diabetic patients. Performance optimization of the developed classification and prediction models was carried out based on different evaluation metrics.

Results. The developed model (Random Forest) achieved the accuracy of 93.1 % (based on laboratory data) and 88 % (based on pysicical examination plus laboratory data). The top five most common predictors in diabetes and prediabetes were found to be glycohemoglobin, basophil count, triglyceride level, waist size, and body mass index (BMI). These results seem logical, since glycohemoglobin is commonly used to check the amount of glucose (sugar) bound to the hemoglobin in the red blood cells. For CVD patients, the most common predictors inlcude eosinophil count (indicative of blood diseases), gamma-glutamyl transferase (GGT), glycohemoglobin, overall oral health, and hand stiffness.

Conclusion. Balancing the dataset and deleting NaN values improved the performance of the developed models. The RFC and XGBoost models achieved higher accuracy using gradient descending order to minimize the loss function. The final prediction is made using a weighted majority vote of all the decisions. The result was an automated system for predicting CVD risk in diabetic patients.

Об авторах

А. Mayya
Saint Petersburg Electrotechnical University; Tishreen University
Сирия

Ali Mayya, Master student at the Department of Bioengineering Systems of Saint Petersburg Electrotechnical University, Bachelor (2019) in Electromechanics – Mechatronics of Tishreen University

Tishreen University, Southern Entrance, Latakia

 



Н. Solieman
Saint Petersburg Electrotechnical University; Tishreen University
Сирия

Hanadi Solieman, Postgraduate student, Assistant at the Department of Bioengineering Systems of Saint Petersburg Electrotechnical University, Assistant at the Mechatronics program for Distinguished of Tishreen University

Tishreen University, Southern Entrance, Latakia



Список литературы

1. Benjamin E. J., Blaha M. J., Chiuve S. E. et al. Heart Disease and Stroke Statistics – 2017 Update. Circulation. 2017, vol. 135, no. 10, pp. e146–e603. doi: 10.1161/CIR.0000000000000485

2. Dinh A., Miertschin S., Young A., Mohanty S. D. A Data-Driven Approach to Predicting Diabetes and Cardiovascular Disease with Machine Learning. BMC Medical Informatics and Decision Making. 2019, vol. 19, no. 1, p. 211.

3. Dounias G., Vemmos K., Alexopoulos E. Medical Diagnosis Of Stroke Using Inductive Machine Learning. Machine Learning and Applications. 1999, 4 p.

4. Flint A. J., Rexrode K. M., Hu F. B., Glynn R. J., Caspard H., Manson J. E., Willet W. C., Rimm E. B. Body Mass Index, Waist Circumference, and Risk of Coronary Heart Disease: A Prospective Study Among Men and Women. Obes Res Clin Pract. 2010, vol. 4, no. 3, pp. e171– e181. doi: 10.1016/j.orcp.2010.01.001

5. Khaw K.-T., Wareham N. Glycated Hemoglobin as a Marker of Cardiovascular Risk. Curr Opin Lipidol. 2006, vol. 17, no. 6, pp. 637–643. doi: 10.1097/MOL.0b013e3280106b95

6. Leon B. M., Maddox T. M. Diabetes and Cardiovascular Disease: Epidemiology, Biological Mechanisms, Treatment Recommendations and Future Research. World J Diabetes. 2015, vol. 6, no. 13, pp. 1246–1258. doi: 10.4239/wjd.v6.i13.1246

7. National Academies of Sciences, Engineering and Medicine; Health and Medicine Division; Food and Nutrition Board; Committee to Review the Dietary Reference Intakes for Sodium and Potassium. Dietary Reference Intakes for Sodium and Potassium. Ed. by Oria M., Harrison M., Stallings V. A. Washington (DC), National Academies Press, 2019, 594 p. doi: 10.17226/25353

8. Ndrepepa G., Kastrati A. Gamma-Glutamyl Transferase and Cardiovascular Disease. Ann Transl Med. 2016, vol. 4, no. 24, p. 481. doi: 10.21037/atm.2016.12.27

9. Parthiban G., Srivatsa S. Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients. Intern. J. of Applied Information Systems. 2012, vol. 3, pp. 25–30.

10. SahBandar I. N., Ndhlovu L. C., Saiki K., Kohorn L. B., Peterson M. M., D'Antoni M. L., Shiramizu B., Shikuma C. M., Chow D. C. Relationship between Circulating Inflammatory Monocytes and Cardiovascular Disease Measures of Carotid Intimal Thickness. J. of Atherosclerosis and Thrombosis. 2019, vol. 27, no. 5, pp. 1–8. doi: 10.5551/jat.49791

11. Semerdjian J., Frank S. An Ensemble Classifier for Predicting the Onset of Type II Diabetes. arXiv:1708.074802017. 2017. doi: 10.48550/arXiv.1708.07480

12. Teimouri M., Ebrahimi E., Alavinia M. Comparison of Various Machine Learning Methods in Diagnosis of Hypertension in Diabetics with/without Consideration of Costs. Iranian J. of Epidemiology. 2016, vol. 11, no. 4, pp. 46–54.

13. National Diabetes Statistics Report. Available at: https://www.cdc.gov/diabetes/data/statistics-report/index.html (accessed 15.01.2022)

14. National Center for Health Statistics. Available at: https://www.cdc.gov/nchs/index.htm (accessed 15.01.2022)

15. Cardiovascular diseases (CVDs). Available at: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) (accessed 15.01.2022)

16. Zeya L. T. Essential Things You Need to Know About F1-Score. Towards Data Science. Available at: https://towardsdatascience.com/essential-things-you-need-toknow-about-f1-score-dbd973bf1a3 (accessed 15.01.2022)

17. DerSarkissian C. Eosinophils and Eosinophil Count Test. Available at: https://www.webmd.com/asthma/eosinophil-count-facts#1 (accessed 15.01.2022)


Рецензия

Для цитирования:


Mayya А., Solieman Н. Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients. Известия высших учебных заведений России. Радиоэлектроника. 2022;25(4):116-122. https://doi.org/10.32603/1993-8985-2022-25-4-116-122

For citation:


Mayya A., Solieman H. Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients. Journal of the Russian Universities. Radioelectronics. 2022;25(4):116-122. (In Russ.) https://doi.org/10.32603/1993-8985-2022-25-4-116-122

Просмотров: 426


Creative Commons License
Контент доступен под лицензией Creative Commons Attribution 4.0 License.


ISSN 1993-8985 (Print)
ISSN 2658-4794 (Online)