Joint prediction of occurrence of heart block and death in patient with myocardial infarction with artificial neural network model

Authors

Negin-sadat Mirian, Morteza SedehiMorteza Sedehi ORCID,*, Soleiman Kheiri, A. li Ahmadi
*Corresponding Author: Email: [email protected]

Koomesh:Vol. 19, issue 1; 241-247
Published online:Sep 01, 2017
Article type:Research Article
Received:Jan 03, 2016
Accepted:Sep 01, 2016
How to Cite:Mirian N, Sedehi M, Kheiri S, Ahmadi AL. Joint prediction of occurrence of heart block and death in patient with myocardial infarction with artificial neural network model. koomesh. 2017;19(1):e151342. doi:

Abstract

Introduction: When it is desired to examine occurrence of two events simultaneously, it is common to use bivariate statistical models such as bivariate logistic regression. Due to the limitations of classical methods in real situations, other methods such as artificial neural networks (ANN) are concerned. The aim of this study was comparing the predictive accuracy of bivariate logistic regression and artificial neural network models in diagnosis of death occurrence and heart block in myocardial infarction patients. Material and Methods: In this study, data was taken from a census in a cross-sectional study in which 263 patients with myocardial infarction cases who admitted to Hajar hospital heart care in 2013 to 2014. Gender, type of stroke, history of diabetes, previous history of hypertension, lipid disorders, history of heart disease, cardiac output fraction, systolic blood pressure, diastolic blood pressure, fasting and non-fasting blood sugar, cholesterol, triglycerides, low-density cholesterol, smoking, type of treatment, the troponin enzymes and insurant type were considered as explanatory variables and occurrence of death and heart block were used as dependent variables. Bivariate logistic regression and neural network model was fitted. Both models were predicted and the accuracy of them were compared. Models were fitted by MATLAB2013a and Zelig in R3.2.2. Results: Predictive accuracy of bivariate logistic regression model was 77.7% for the training and 78.48% for the test data. In ANN model, LM and OSS algorithms had best performance with 83.69% and 83.15% predictive accuracy for training data and 84.81% and 83.54% for testing data, respectively. Conclusion: This research showed that the neural network method is more accurate than bivariate logistic regression to joint predicting the occurrence of death and heart block in patients with myocardial infarction.

References

  • 1.
    Regan M, Catalano P. Likelihood models for clustered binary and continuous outcomes Application to Developmental toxicology. Biometrics 1999; 55: 760-768.
  • 2.
    Teixeira-Pinto A, Normand T. Correlated bivariate continuous and binary outcomes issues and applications. Stat Med 2009; 28: 753-773.
  • 3.
    Anderson A. An introduction to neural network, Cambridge, MA: MIT press; 1995: 795-851.
  • 4.
    Gupta P, Bikrampal K. Accuracy enhancement of heart disease diagnosis system using neural network and genetic algorithm. Int J Adv Res Comput Sci Softw Eng 2014; 4: 160-166.
  • 5.
    Menhaj MB, editor. Fundamentals of neural networks. 8th ed. Amir Kabir Univ pub; 2012 (Persian).
  • 6.
    Babaei M, Mohammad Khan Kermanshahi S, Alhani F. Influence of discharge planning on anxiety levels in patients with myocardial infarction. Koomesh 2011; 12: 272-278.
  • 7.
    Anman EM, Braunwald E. Acute myocardial infarction. In: Braunwald E, Zips D, Libby P, editors. Heart Disease, 6th ed. Philadelphia: W.B. Saunders Com; 2001; P: 1114-1219.
  • 8.
    Ahmadi A, Soori H, Mehrabi Y, Etemad K, Khaledifar A. Epidemiologic pattern of myocardial infarction and modeling risk factors relevant to in-hospital mortality: the first results from Iranian Myocardial Infarction Registry. Kardiologia Polska 2015; 73: 451-457.
  • 9.
    Asgari M R, Jafarpoor H, Soleimani M, Ghorbani R, Askandarian R, Jafaripour I. Effects of early mobilization program on depression of patients with myocardial infarction hospitalized in CCU. Koomesh 2015; 16: 175-118.
  • 10.
    Sadri P, Khaledifar A, Ahmadi A. Survey of the incidence rate of complete bundle branch block in patients with acute myocardial infarction in CCU ward in Hagar hospital. [Dissertation] Shahre Kord. Shahre kord Univ Med Sci 2013-14 (Persian).
  • 11.
    Kay JW, Titterington DM, editors. Statistics and neural networks: Advanced at the interface. Oxford: Oxford University Press; 1999.
  • 12.
    Wang S. An insight into the standard back propagation neural network model for regression analysis. J Mgmt Sci 1998; 26: 133-140.
  • 13.
    Sedehi M, Mehrabi Y, Kazemnejad A, Joharimajd V, Hadaegh F. Artificial neural network design for modeling of mixed bivariate outcomes in medical research data. Iranian J Epidemiol 2010; 6: 28-39. (Persian).
  • 14.
    Parsaeian M, Mohammad K, Mahmoudi M, Zeraati H. Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey. Iran J Public Health 2012; 41: 86-92.
  • 15.
    Hosseini Teshnizi S, Ayatollahi SM. A comparison of logistic regression model and artificial neural networks in predicting of students academic failure. Acta Inrorm Med 2015; 23: 296-300.
  • 16.
    Sedehi M, Mehrabi Y, Kazemnejad A, Johari-majd V, Hadaegh F. Design of artificial neural network for joint predicting of metabolic syndrome and HOMA-IR. Daneshvar 2009; 17: 29-36. (Persian).
  • 17.
    Adeli M, et al. Application of artificial neural network model in predicting the mixed response of atherosclerosis disease. RJMS 2013; 20: 20-28. (Persian).
  • 18.
    Biglarian A, Hajizadeh E, Kazemnejad A, Zali MR. Application of artificial neural network in predicting the survival rate of gastric cancer patients. Iran J Public Health 2011; 40: 80-86.##.

Copyright

© 2017, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.

Similar Articles

20
Jun
2010

Comparison of artificial neural network and Cox regression models in survival prediction of gastric cancer patients

Akbar Biglarian,
Ebrahim HajiZadeh,
Anoshirvan Kazemnejad

Biglarian A, HajiZadeh E, Kazemnejad A. Comparison of artificial neural network and Cox regression models in survival prediction of gastric cancer patients. koomesh. 2010;11(3):e153802. doi:

30
Jun
2017
Study on the Efficiency of a Multi-layer Perceptron Neural Network Based on the Number of Hidden Layers and Nodes for Diagnosing Coronary- Artery Disease

Study on the Efficiency of a Multi-layer Perceptron Neural Network Based on the Number of Hidden Layers and Nodes for Diagnosing Coronary- Artery Disease

Hamid Moghaddasi,
Bahareh Ahmadzadeh,
Reza Rabiei,
Mohammad Farahbakhsh

Moghaddasi H, Ahmadzadeh B, Rabiei R, Farahbakhsh M. Study on the Efficiency of a Multi-layer Perceptron Neural Network Based on the Number of Hidden Layers and Nodes for Diagnosing Coronary- Artery Disease. Jentashapir J Cell Mol Biol. 2017;8(3):e63032. doi: https://doi.org/10.5812/jjhr.63032

27
Feb
2015

Using machine learning techniques to differentiate acute coronary syndrome

Sougand Setareh,
Ali Asghar Safaei,
Farid Najafi

Setareh S, Safaei AA, Najafi F. Using machine learning techniques to differentiate acute coronary syndrome. J Kermanshah Univ Med Sci. 2015;18(11):e73997. doi: https://doi.org/10.22110/jkums.v18i11.2023

30
Sep
2015

In-Hospital Case Fatality Rate and Cox Proportional-Hazards Model for Risk Factors of Mortality Due to Myocardial Infarction in Iran’s Hospitals: A National Study

Ali Ahmadi,
Hamid Soori,
Arsalan Khaledifar

Ahmadi A, Soori H, Khaledifar A. In-Hospital Case Fatality Rate and Cox Proportional-Hazards Model for Risk Factors of Mortality Due to Myocardial Infarction in Iran’s Hospitals: A National Study. Int Cardiovasc Res J. 2017;9(3):e11086. doi:

15
Jun
2022

Comparison of Random Forest and Artificial Neural Network Models to Evaluate Diagnostic Factors in the Necessity to Perform Angiography

Parastoo Golpour,
Mohammad Tajfard,
Majid Ghayour-Mobarhan,
Mohsen Moohebati,
Ali Taghipour,
habibollah Esmaily
,et al.

Golpour P, Tajfard M, Ghayour-Mobarhan M, Moohebati M, Taghipour A, et al. Comparison of Random Forest and Artificial Neural Network Models to Evaluate Diagnostic Factors in the Necessity to Perform Angiography. Int Cardiovasc Res J. 2022;16(2):e122437. doi:

More by these authors

Negin-sadat MirianPubMedScholar
Morteza SedehiPubMedScholar
Soleiman KheiriPubMedScholar
A. li AhmadiPubMedScholar
Share
Cited by
Metrics