Determining the Risk Factors and Presenting a Prognostic Model for Pulmonary Embolism in Hospitalized Patients using Bayesian Networks

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Feizmanesh F.; SAFAEI A.A.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Epidemiology Year:2018 | Volume:14 | Issue:3 Page(s): 272-282

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

320

Download:

Cites:

Information Journal Paper

Title

Determining the Risk Factors and Presenting a Prognostic Model for Pulmonary Embolism in Hospitalized Patients using Bayesian Networks

Author(s)

Feizmanesh F. | SAFAEI A.A. | Issue Writer Certificate

Keywords

Pulmonary embolismQ1

Machine learningQ1

Prediction modelsQ1

Bayesian networksQ1

Decision TreeQ1

Logistic RegressionQ2

Sequential minimal optimizationQ1

Risk factorsQ3

Abstract

Background and Objectives: Pulmonary embolism is a potentially fatal and prevalent event that has led to a gradual increase in the number of hospitalizations in recent years. For this reason, it is one of the most challenging diseases for physicians. The main purpose of this paper was to report a research project to compare different data mining algorithms to select the most accurate model for predicting Pulmonary embolism in hospitalized patients. This model would provide the knowledge needed by the medical staff fir better decision making. Methods: In this research, we designed a prediction model using different methods of Machine learning that would best predict the probability of Pulmonary embolism in patients at risk. Among data mining algorithms, Bayesian network, decisions tree (J48), Logistic Regression (LR), and Sequential minimal optimization (SMO) were used. The data used in the study included Risk factors and past history of patients admitted to the Lung Department of Shariati Hospital, Tehran, Iran. Results: The results showed that the accuracy and specificity of all Prediction models were satisfactory. The Bayesian model had the highest sensitivity in predicting Pulmonary embolism. Conclusion: Although the results showed a little difference in the performance of Prediction models, the Bayesian model is a more appropriate tool to predict the occurrence of Pulmonary embolism in hospitalized patients in this type of data. It can be considered a supportive approach along medical decisions to improve disease prediction.

Cites

No record.

References

No record.

Cite

APA: Copy

Feizmanesh, F., & SAFAEI, A.A.. (2018). Determining the Risk Factors and Presenting a Prognostic Model for Pulmonary Embolism in Hospitalized Patients using Bayesian Networks. IRANIAN JOURNAL OF EPIDEMIOLOGY, 14(3 ), 272-282. SID. https://sid.ir/paper/376200/en

Vancouver: Copy

Feizmanesh F., SAFAEI A.A.. Determining the Risk Factors and Presenting a Prognostic Model for Pulmonary Embolism in Hospitalized Patients using Bayesian Networks. IRANIAN JOURNAL OF EPIDEMIOLOGY[Internet]. 2018;14(3 ):272-282. Available from: https://sid.ir/paper/376200/en

IEEE: Copy

F. Feizmanesh, and A.A. SAFAEI, “Determining the Risk Factors and Presenting a Prognostic Model for Pulmonary Embolism in Hospitalized Patients using Bayesian Networks,” IRANIAN JOURNAL OF EPIDEMIOLOGY, vol. 14, no. 3 , pp. 272–282, 2018, [Online]. Available: https://sid.ir/paper/376200/en

Related Journal Papers