Volume 14, Issue 3 (Vol.14, No.3, 2018)
irje 2018, 14(3): 272-282 |
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Feizmanesh F, Safaei A. Determining the Risk Factors and Presenting a Prognostic Model for Pulmonary Embolism in Hospitalized Patients using Bayesian Networks. irje 2018; 14 (3) :272-282
URL: http://irje.tums.ac.ir/article-1-6129-en.html
URL: http://irje.tums.ac.ir/article-1-6129-en.html
1- Master of Science, Tarbiat Modares University, Medical Informatics, Department of Medical Informatics, Faculty of Medical Sciences, Tehran, Iran
2- Assistant Professor, Tarbiat Modares University, Computer Engineering Software, Department of Medical Informatics, Faculty of Medical Sciences, Tehran, Iran ,aa.safaei@modares.ac.ir
2- Assistant Professor, Tarbiat Modares University, Computer Engineering Software, Department of Medical Informatics, Faculty of Medical Sciences, Tehran, Iran ,
Abstract: (5457 Views)
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.
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.
Keywords: PPulmonary embolism, Machine learning, Prediction models, Bayesian networks, Decision Tree, Logistic Regression, Sequential minimal optimization, Risk factors
Type of Study: Research |
Subject:
Epidemiology
Received: 2019/01/7 | Accepted: 2019/01/7 | Published: 2019/01/7
Received: 2019/01/7 | Accepted: 2019/01/7 | Published: 2019/01/7
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