Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):

Share By

Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran Publisher Pubmed

Summary: A study found similar COVID-19 symptoms across Iran, with AI models aiding accurate diagnosis. #COVID19 #MedicalAI

Mohammadi F^{1, 2} ; Pourzamani H^{1, 2} ; Karimi H¹ ; Mohammadi M³ ; Mohammadi M³ ; Ardalan N⁵ ; Khoshravesh R⁶ ; Pooresmaeil H⁷ ; Shahabi S⁸ ; Sabahi M⁹ ; Sadat Miryonesi F¹⁰ ; Najafi M¹¹ ; Yavari Z¹² ; Mohammadi F^{1, 2} Show All Authors

Mohammadi F^{1, 2}
Pourzamani H^{1, 2}
Karimi H¹
Mohammadi M³
Mohammadi M³
Ardalan N⁵
Khoshravesh R⁶
Pooresmaeil H⁷
Shahabi S⁸
Sabahi M⁹
Sadat Miryonesi F¹⁰
Najafi M¹¹
Yavari Z¹²
Mohammadi F^{1, 2}
Teiri H^{1, 2}
Jannati M¹⁴

Source: Biomedical Journal Published:2021

Abstract

Background: COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patients in 6 provinces of Iran. Also, multilayer perceptron (MLP) neural network and Logistic Regression (LR) models were applied for the diagnosis of COVID-19. Methods: A total of 1043 patients with suspected COVID-19 infection in Iran participated in this study. 29 characteristics, symptoms and underlying disease were obtained from hospitalized patients. Afterwards, we compared the obtained data between confirmed cases. Furthermore, the data was applied for building the ANN and LR models to diagnosis the infected patients by COVID-19. Results: In 750 confirmed patients, Common symptoms were: fever (%) >37.5 °C, cough, shortness of breath, fatigue, chills and headache. The most common underlying diseases were: hypertension, diabetes, chronic obstructive pulmonary disease and coronary heart disease. Finally, the accuracy of the ANN model to the diagnosis of COVID-19 infection was higher than the LR model. Conclusion: The prevalent symptoms and underlying diseases of COVID-19 patients were similar in different provinces, but the incidence of symptoms was significantly different from each other. Also, the study demonstrated that ANN and LR models have a high ability in the diagnosis of COVID-19 infection. © 2021 Chang Gung University

Related Docs

View other Related Docs

1. Age-Dependent Clinical Features and Prognosis of Covid-19 Patients, Tanaffos (2021)

2. Clinical Display, Diagnostics and Genetic Implication of Novel Coronavirus (Covid-19) Epidemic, European Review for Medical and Pharmacological Sciences (2020)

3. The Clinical Manifestation and Outcome of Covid-19 in Patients With a History of Ischemic Heart Disease; a Retrospective Case-Control Study, BMC Cardiovascular Disorders (2023)

Experts (# of related papers)

View all Related Experts

Ramin Sami (6)

Marjan Mansourian (5)

Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran

Other Related Docs

4. The Severity of Symptoms and Outcomes of Patients Admitted With Rheumatic Diseases and Covid-19; a Prospective Cohort Study in Iran, Immunopathologia Persa (2021)

5. Current Status of Covid-19 Pandemic; Characteristics, Diagnosis, Prevention, and Treatment, Journal of Research in Medical Sciences (2020)

6. Risk Factors, Clinical, and Laboratory Characteristics of Patients With a Diagnosis of Covid-19, Journal of Emergency Practice and Trauma (2022)

7. Association of the Medication Protocols and Longitudinal Change of Covid-19 Symptoms: A Hospital-Based Mixed-Statistical Methods Study, Journal of Biopharmaceutical Statistics (2024)

8. Machine Learning Model Optimization for Removal of Steroid Hormones From Wastewater, Chemosphere (2023)

9. Factors Associated With Mortality in Hospitalized Cardiovascular Disease Patients Infected With Covid-19, Immunity, Inflammation and Disease (2022)

10. Clinical Symptoms of Covid-19 and Their Association With Disease Outcome, Advanced Biomedical Research (2022)

11. A One-Year Hospital-Based Prospective Covid- 19 Open-Cohort in the Eastern Mediterranean Region: The Khorshid Covid Cohort (Kcc) Study, PLoS ONE (2020)

12. Factors Related to the Hospital Admissions of Covid-19 Patients in the Intensive Care Unit of Selected Iranian Navy Hospitals, Iranian Journal of War and Public Health (2021)

13. Exponentially Increasing Trend of Infected Patients With Covid-19 in Iran: A Comparison of Neural Network and Arima Forecasting Models, Iranian Journal of Public Health (2020)

14. Epidemiological Status and Health System Screening of Covid 19 Hospitalized Patients in Isfahan, Iran, Medical Journal of the Islamic Republic of Iran (2022)

15. Covid-19, an Early Investigation From Exposure to Treatment Outcomes in Tehran, Iran, Journal of Research in Medical Sciences (2021)

16. Association Between Clinical Characteristics and Laboratory Findings With Outcome of Hospitalized Covid-19 Patients: A Report From Northeast Iran, Interdisciplinary Perspectives on Infectious Diseases (2021)

17. From Symptoms to Survival: Tracking Covid-19 Patient Outcomes in an Outpatient Clinic; Od Simptoma Do Prezivljavanja: Pracenje Ishoda Pacijenata Sa Kovidom 19 U Ambulantnoj Klinici, Medicinski Casopis (2024)

18. Epidemiology, Virology, Clinical Features, Diagnosis, and Treatment of Sars-Cov-2 Infection, Journal of Experimental and Clinical Medicine (Turkey) (2021)

19. Epidemiological Review of the Coronavirus Disease 2019 (Covid-19) Pandemic up to 15 March 2020, Journal of Isfahan Medical School (2020)

20. An Experimental in Silico Study on Covid-19: Response of Neutrophil-Related Genes to Antibiotics, Health Science Reports (2022)

Style	Citing Format
MLA	Mohammadi F, et al.. "Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran." Biomedical Journal, vol. 44, no. 3, 2021, pp. 304-316.
APA	Mohammadi F, Pourzamani H, Karimi H, Mohammadi M, Mohammadi M, Ardalan N, Khoshravesh R, Pooresmaeil H, Shahabi S, Sabahi M, Sadat Miryonesi F, Najafi M, Yavari Z, Mohammadi F, Teiri H, Jannati M (2021). Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran. Biomedical Journal, 44(3), 304-316.
Chicago	Mohammadi F, Pourzamani H, Karimi H, Mohammadi M, Mohammadi M, Ardalan N, Khoshravesh R, et al.. "Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran." Biomedical Journal 44, no. 3 (2021): 304-316.
Harvard	Mohammadi F et al. (2021) 'Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran', Biomedical Journal, 44(3), pp. 304-316.
Vancouver	Mohammadi F, Pourzamani H, Karimi H, Mohammadi M, Mohammadi M, Ardalan N, et al.. Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran. Biomedical Journal. 2021;44(3):304-316.
BibTex	@article{ author = {Mohammadi F and Pourzamani H and Karimi H and Mohammadi M and Mohammadi M and Ardalan N and Khoshravesh R and Pooresmaeil H and Shahabi S and Sabahi M and Sadat Miryonesi F and Najafi M and Yavari Z and Mohammadi F and Teiri H and Jannati M}, title = {Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran}, journal = {Biomedical Journal}, volume = {44}, number = {3}, pages = {304-316}, year = {2021} }
RIS	TY - JOUR AU - Mohammadi F AU - Pourzamani H AU - Karimi H AU - Mohammadi M AU - Mohammadi M AU - Ardalan N AU - Khoshravesh R AU - Pooresmaeil H AU - Shahabi S AU - Sabahi M AU - Sadat Miryonesi F AU - Najafi M AU - Yavari Z AU - Mohammadi F AU - Teiri H AU - Jannati M TI - Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran JO - Biomedical Journal VL - 44 IS - 3 SP - 304 EP - 316 PY - 2021 ER -