Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods

Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods Publisher

Khodabakhsh P¹ ; Asadnia A² ; Moghaddam AS³ ; Khademi M⁴ ; Shakiba M⁵ ; Maher A⁶ ; Salehian E⁷

Show Affiliations

1. Department of IT and Computer Engineering, Azad Islamic University South Tehran Branch, Tehran, Iran
2. Institute of Social Sciences, Department of Business Analytics, Marmara University, Istanbul, Turkey
3. School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
4. Department of Applied Mathematics, Azad Islamic University South Tehran Branch, Tehran, Iran
5. Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
6. Department of Health Policy, Economics and Management, School of Management and Medical Education, Shahid Beheshti University of Medical Sciences, Tehran, Iran
7. Department of Information Technology, Medical Science of Shahid, Beheshti University, Tehran, Iran

Source: Journal of Diabetes and Metabolic Disorders Published:2023

Abstract

Background: Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority. Method and Material: The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records. Results: The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model. Conclusion: Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51–80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients. © 2023, The Author(s), under exclusive licence to Tehran University of Medical Sciences.

Related Docs

View other Related Docs

1. Well-Controlled Vs Poorly-Controlled Diabetes in Patients With Covid-19: Are There Any Differences in Outcomes and Imaging Findings?, Diabetes Research and Clinical Practice (2020)

2. Covid-19-Specific Worries Among People With Type 2 Diabetes Following the Continuation of the Pandemic and Occurrence of Multiple Waves of Covid-19 in Iran, Journal of Diabetes and Metabolic Disorders (2022)

3. Metabolic Syndrome and Covid-19; Clinical Complications and Challenges, Journal of Isfahan Medical School (2021)

4. Intensive Care Unit Admission and Associated Factors in Patients Hospitalised for Covid-19: A National Retrospective Cohort Study in Iran, BMJ Open (2023)

5. Predictors of the Prolonged Recovery Period in Covid-19 Patients: A Cross-Sectional Study, European Journal of Medical Research (2021)

6. Physical Activity Behavior During the Covid-19 Outbreak in Individuals With Type 2 Diabetes: Role of Social Support and Other Covariates, International Journal of Endocrinology and Metabolism (2022)

7. Triage of Patients With Covid19-: Using Ensemble Learning Method for Risk Factor Analysis and Death Prediction, Koomesh (2024)

8. Coronavirus Disease (Covid-19): 10 Questions and Discussion Points for Diabetes and Covid-19, Frontiers in Emergency Medicine (2021)

Experts (# of related papers)

View all Related Experts

Bagher Larijani (4)

Nima Rezaei (3)

Style	Citing Format
MLA	Khodabakhsh P, et al.. "Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods." Journal of Diabetes and Metabolic Disorders, vol. 22, no. 2, 2023, pp. 1177-1190.
APA	Khodabakhsh P, Asadnia A, Moghaddam AS, Khademi M, Shakiba M, Maher A, Salehian E (2023). Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods. Journal of Diabetes and Metabolic Disorders, 22(2), 1177-1190.
Chicago	Khodabakhsh P, Asadnia A, Moghaddam AS, Khademi M, Shakiba M, Maher A, Salehian E. "Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods." Journal of Diabetes and Metabolic Disorders 22, no. 2 (2023): 1177-1190.
Harvard	Khodabakhsh P et al. (2023) 'Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods', Journal of Diabetes and Metabolic Disorders, 22(2), pp. 1177-1190.
Vancouver	Khodabakhsh P, Asadnia A, Moghaddam AS, Khademi M, Shakiba M, Maher A, et al.. Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods. Journal of Diabetes and Metabolic Disorders. 2023;22(2):1177-1190.
BibTex	@article{ author = {Khodabakhsh P and Asadnia A and Moghaddam AS and Khademi M and Shakiba M and Maher A and Salehian E}, title = {Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods}, journal = {Journal of Diabetes and Metabolic Disorders}, volume = {22}, number = {2}, pages = {1177-1190}, year = {2023} }
RIS	TY - JOUR AU - Khodabakhsh P AU - Asadnia A AU - Moghaddam AS AU - Khademi M AU - Shakiba M AU - Maher A AU - Salehian E TI - Prediction of In-Hospital Mortality Rate in Covid-19 Patients With Diabetes Mellitus Using Machine Learning Methods JO - Journal of Diabetes and Metabolic Disorders VL - 22 IS - 2 SP - 1177 EP - 1190 PY - 2023 ER -

Science Communicator Platform

Authors

Authors Affiliations

Abstract