A Simple Risk Model of the Incidence of Atrial Fibrillation and Coronary Artery Disease ‎Using a Data Mining Algorithm: Risk Factor Prediction

Document Type : Original Article

Authors

1 Department of Biology, Falavrjan Branch, Islamic Azad University, Isfahan, Iran

2 Department of Molecular and Cell Biochemistry, Falavarjan Branch, Islamic Azad University, Isfahan, Iran

3 Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

Abstract

Purpose:
Cardiovascular disease is one of the most important causes of death worldwide. Using data mining methods to create predictive models to identify people at risk to prevent complications from cardiovascular diseases will be very effective. The aim of this research is to predict the probability of infection in people with coronary heart disease and atrial fibrillation using support vector machine, neural network and decision tree algorithms based on factors affecting the disease.
Methods:
This analytical research includes 300 records. The information required for this study was collected in 1400 using the records of patients admitted to Chamran and Khurshid hospitals in Isfahan. For data analysis, the information includes laboratory, demographic and family history sections using the CRISP method, the Cross Industry Standard Process for Data Mining (CRISP). Decision trees, neural networks and support vector machines are also used in the modeling section.
Results:
The sensitivity and specificity in the neural network data mining algorithm  are 87.5 and 71.11 respectively, 92.85 and 80 in the decision tree algorithm and 88.88 and 75 in the support vector machine. Therefore, the decision tree algorithm has a better performance for predicting the probability of heart and coronary artery diseases and atrial fibrillation. Also it was found that stress, high BMI, high blood pressure and type of job had the greatest effect on the occurrence of heart and coronary artery diseases and cardiac arrhythmias.
Conclusion:
In the current study, the decision tree has the highest performance, so it can be used to determine the probability of coronary heart and vascular problems and atrial fibrillation.

Keywords

References

  1. Safdari R, Ghazi Saeedi M, Gharooni M, Nasiri M, Argi G. Comparing performance of decision tree and neural network in predicting myocardial infarction. Journal of Paramedical Sciences & Rehabilitation 2014; 3(2): 26-35. [Persian]
  2. Shahid AH, Singh MP. A novel approach for coronary artery disease diagnosis using hybrid particle swarm optimization based emotional neural network. Biocybernetics and Biomedical Engineering 2020; 40(4):1568-1568.
  3. Ilayaraja M, Meyyappan T. Efficient data mining method to predict the risk of heart diseases through frequent itemsets. Procedia Computer Science. 2015; 1(70): 586-592.
  4. Alonso A, Krijthe BP, Aspelund T, Stepas KA, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE‐AF consortium. JAm Heart Assoc 2013; 2(2): e000102.
  5. Bahrami B, Shirvani MH. Prediction and diagnosis of heart disease by data mining techniques. Journal of Multidisciplinary Engineering Science and Technology (JMEST) 2015; 2(2): 164-168.
  6. Yaribeygi H, Taghipour H, Taghipour H. Prevalence of cardiovascular risk factors in patients undergoing CABG: brief report. Med J 2014; 72(8): 570-574. [Persian]
  7. Daraei A, Hamidi H. An efficient predictive model for myocardial infarction using cost-sensitive j48 model. Iranian Journal of public health 2017;46(5): 68.
  8. Murakami N, Tanno M, Kokubu N, Nishida J, et al. Distinct risk factors of atrial fibrillation in patients with and without coronary artery disease: a cross-sectional analysis of the BOREAS-CAG Registry data. Open Heart 2017; 4(1): e000573.
  9. Sharifi M. Computational approaches to understand the adverse drug effect on potassium, sodium and calcium channels for predicting TdP cardiac arrhythmias. J Mol Graph Model 2017; 76: 152-160.
  10. Vijayashree J, Sriman Narayana Iyengar NC. Heart disease prediction system using data mining and hybrid intelligent techniques: A review. International Journal of Bio-Science and Bio-Technology 2016; 8(4):139-148.
  11. Yadav C, Lade S, Suman MK. Predictive analysis for the diagnosis of coronary artery disease using association rule miningInternational Journal of Computer Applications 2014; 87(4): 0975-8887.  
  12. Veisi H, Qaidsharf HR, Ebrahimi M. Improving the efficiency of machine learning algorithms in heart disease diagnosis by optimizing data and features. Soft computing 2019; 8(1): 70-85. [Persian]
  13. Ayatollahi, H., Gholamhosseini L, Salehi M. Comparing the performance of data mining algorithms in predicting coronary artery disease. Journal of Health and Biomedical Informatics 2017; 5(2): 252-264. [Persian]
  14. Kazemi M, Mehdizadeh H, Shiri A. Heart disease forecast using neural network data mining technique. Journal of Ilam University of Medical Sciences 2017; 25(1): 20-32. [Persian]
  15. Mazaheri S, Ashuri M, Bechari Z. Prediction of heart disease treatment method using data mining algorithms. Journal of Payavard Salamat 2017; 11(3); 287-296. [Persian]
  16. Kumar MN, Koushik, KVS, Deepak K. Prediction of heart diseases using data mining and machine learning algorithms and tools. International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2018; 3(3), 887-898.
  17. Salve I, Kumar D, Borikar A. Heart Disease Prediction using Data Mining Techniques. Journal of Algebraic Statistic 2022; 13(3): 2250-2259.
  18. Huang PS, Tseng YH, Tsai CF, Chen J. et al. Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease. IJERPH 2022; 10(2): 10.3390/biomedicines10020394 .
  19. Chen JIZ, Hengjinda P. Early prediction of coronary artery disease (CAD) by machine learning method-a comparative study. IRO Journals 2021; 3(01): 17-33.
  20. Imamovic D, Babovic E, Bijedic N. Prediction of mortality in patients with cardiovascular disease using data mining methods IS(INFOTEH) 2020 (pp. 1-4).
  21. Vindhya L, Beliray PA, Sravani CR, Divya DR. Prediction of Heart Disease Using Machine Learning Techniques. International Journal of Research in Engineering, Science and Management 2020; 3(8): 325-326.
  22. Choi Y, Boo Y. Comparing logistic regression models with alternative machine learning methods to predict the risk of drug intoxication mortality. IJERPH 2020;17(3): 89.
  23. Niaksu O. CRISP data mining methodology extension for medical domain. Baltic Journal of Modern Computing 2015; 3(2): 92.
  24. Mathan K, Kumar PM, Panchatcharam P, Manogaran G, Varadharajan R. A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design automation for embedded systems 2018; 22(3): 225-242.
  25. Zabbah I, Hassaanzadeh M. The effect of continuous parameters on the diagnosis of coronary artery disease using artificial neural networks. Journal of Health Chimes 2017; 4(4): 29-39. [Persion]
  26. Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association 2017; 24(2): 361-370.
  27. Cherfi A, Nouira K, Ferchichi A. Very fast C4. 5 decision tree algorithm. Applied Artificial Intelligence 2018; 32(2): 1-19.
  28. Austin PC, Tu JV, Ho JE, Levy D, Lee DS. Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. Journal of clinical epidemiology 2013; 66(4): 398-407.
  29. Rani KU. Analysis of heart diseases dataset using neural network approach. IJDKP 2011; 1(5):1-8.
  30. Mazaheri S, Ashoori M, Bechari Z. A model to predict Heart disease treatment using data mining. Journal of Payavard Salamat 2017; 11(3): 287-296. [Persion]
  31. Karimian B, Khadem Watan K, Seyed Mohammadzadeh MH, Alinejad V, et al. Investigating the relationship between the severity of coronary artery disease with uric acid and Fetuin A in patients undergoing coronary angiography. Journal of Medical Sciences Studies 2014; 26 (8): 671-663.
  32. Sabbagh Gol H. Detection of Coronary Artery Disease Using C4.5 Decision Tree .Journal of Health and Biomedical Informatics Medical Informatics Research Center 2017; 3(4): 287-299. [Persian]
  33. Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, et al. A data mining approach for diagnosis of coronary artery disease. Computer methods and programs in biomedicine 2013; 11(1):52-61. [Persian]
Journal of Paramedical Sciences & Rehabilitation
Volume 12, Issue 1
June 2023
Pages 42-52

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