LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,092
تعداد مقالات10,022
تعداد مشاهده مقاله27,212,038
تعداد دریافت فایل اصل مقاله7,469,027
Dekamin, Azam, Sheibatolhamdi, Ahmad. (1395). A Data Mining Approach for Coronary Artery Disease Prediction in Iran. سامانه مدیریت نشریات علمی, 3(1), 29-38. doi: 10.18869/nrip.jamsat.3.1.29
Azam Dekamin; Ahmad Sheibatolhamdi. "A Data Mining Approach for Coronary Artery Disease Prediction in Iran". سامانه مدیریت نشریات علمی, 3, 1, 1395, 29-38. doi: 10.18869/nrip.jamsat.3.1.29
Dekamin, Azam, Sheibatolhamdi, Ahmad. (1395). 'A Data Mining Approach for Coronary Artery Disease Prediction in Iran', سامانه مدیریت نشریات علمی, 3(1), pp. 29-38. doi: 10.18869/nrip.jamsat.3.1.29
Dekamin, Azam, Sheibatolhamdi, Ahmad. A Data Mining Approach for Coronary Artery Disease Prediction in Iran. سامانه مدیریت نشریات علمی, 1395; 3(1): 29-38. doi: 10.18869/nrip.jamsat.3.1.29

A Data Mining Approach for Coronary Artery Disease Prediction in Iran

Journal of Advanced Medical Sciences and Applied Technologies
مقاله 5، دوره 3، شماره 1، خرداد 2017، صفحه 29-38    اصل مقاله (774 K)
نوع مقاله: Original Articles
شناسه دیجیتال (DOI): 10.18869/nrip.jamsat.3.1.29
نویسندگان
Azam Dekamin* 1؛ Ahmad Sheibatolhamdi2
1Department of Information Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2Department of Industrial Management, Faculty of Humanities, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
چکیده
Objectives: Coronary artery disease (CAD) is caused by atherosclerosis in coronary arteries and results in cardiac arrest and heart attack. Angiography is one of the most accurate methods to diagnose Heart disease, it incurs high expenses and comes with side effects. Data mining is the extraction of hidden predictive information and unknown data, patterns, relationships and knowledge by exploring the large data sets which are difficult to find and detect with traditional statistical methods. One of the biggest problems that prevent pattern recognition from functioning rapidly and effectively are the noisy and inconsistent data in databases. The present study intends to provide a data preparation method based on clustering algorithms for diagnosis Coronary artery disease with higher efficiency and fewer errors. Materials: In this study, the data under investigation was collected from a number of 303 persons referring to the heart unit in one of Tehran-based hospitals within the time interval 2011 to 2013. It included 54 features. K-means algorithm is used for clustering based data preprocessing system for elimination of noisy and inconsistent data and Naïve Bayes, K nearest neighbor and Decision tree are used for classification. Another two feature subset selection methods for cleaning data are also used to make a comparison between clustering based method and attribute selection method. Rapid Miner Software was adopted to conduct this study.Results: Findings of this research indicated that the suggested model will have the highest efficiency, 90.91. According to the results, the proposed method of performance is highly successful compared to other results attained and seems effective for pattern recognition applications.Conclusion: With these results, the proposed method can be used in the diagnosis of coronary artery disease 
کلیدواژه‌ها
Coronary artery disease؛ Angiography؛ Data mining؛ Stacking؛ Random forest
مراجع
  1. Srinivas K, Rao GR, Govardhan A. Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques. Paper presented at: The 5th International Conference on Computer Science & Education. 2010 August 24-27; Hefei, China. doi: 10.1109/iccse.2010.5593711
  2. Vanisree K, Singaraju J. Decision support system for congenital heart disease diagnosis based on signs and symptoms using neural networks. International Journal of Computer Applications. Foundation of Computer Science; 2011; 19(6):6–12. doi: 10.5120/2368-3115
  3. Elsayad A, Fakr M. Diagnosis of cardiovascular diseases with Bayesian classifiers. Journal of Computer Science. 2015; 11(2):274-82.
  4. Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, et al. A data mining approach for diagnosis of coronary artery disease. Computer Methods and Programs in Biomedicine. 2013; 111(1):52–61. doi: 10.1016/j.cmpb.2013.03.004
  5. Polat K, Güneş S. A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS. Computer Methods and Programs in Biomedicine. 2007; 88(2):164–74. doi: 10.1016/j.cmpb.2007.07.013
  6. Rajkumar M, Reena GS. Diagonsis of heaer disease using datamining algorithm. Global journal of Computer Science and Technology. 2010; 10(10):38-43.
  7. Akyol K, Çalik E, Bayir Ş, Şen B, Çavuşoğlu A. Analysis of demographic characteristics creating coronary artery disease susceptibility using random forests classifier. Procedia Computer Science. 2015; 62:39–46. doi: 10.1016/j.procs.2015.08.407
  8. Yilmaz N, Inan O, Uzer MS. A new data preparation method based on clustering algorithms for diagnosis systems of heart and diabetes diseases. Journal of Medical Systems. 2014; 38(5):48. doi: 10.1007/s10916-014-0048-7
  9. Nahar J, Imam T, Tickle KS, Chen YPP. Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications. 2013; 40(4):1086–93. doi: 10.1016/j.eswa.2012.08.028
  10. Paliwal P, Malviya M. An efficient method for predicting heart disease problem using fitness value. International Journal of Computer Science and Information Technologies. 2015; 6(2):1290-3.
  11. Safdari R, Ghazi Saeedi M, Gharooni M, Nasiri M, Arji G. [Comparing performance of decision tree and neural network in predicting myocardial infarction (Persian)]. Journal of Paramedical Science and Rehabilitation. 2014; 3(2):26-37.
  12. Rao VSH, Kumar MN. A new intelligence-based approach for computer-aided diagnosis of dengue fever. IEEE Transactions on Information Technology in Biomedicine. 2012; 16(1):112–8. doi: 10.1109/titb.2011.2171978
  13. Parthiban L, Subramanian R. Intelligent heart disease prediction system using CANFIS and genetic algorithm. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering; 2007; 1(5):278-81.
  14. Han J, Pei J, Kamber M. Data mining: Concepts and techniques. Philadelphia: Elsevier; 2011.
  15. Subbalakshmi G, Ramesh K, Rao MC. Decision support in heart disease prediction system using naive bayes. Indian Journal of Computer Science and Engineering. 2011; 2(2):170-6.
  16. Detrano RC, Wong ND, Doherty TM, Shavelle RM, Tang W, Ginzton LE, et al. Coronary calcium does not accurately predict near-term future coronary events in high-risk adults. Circulation. 1999; 99(20):2633–8. doi: 10.1161/01.cir.99.20.2633
  17. Cheung N. Machine learning techniques for medical analysis. School of Information Technology and Electrical Engineering [PhD dissertation]. Queenland: University of Queenland.
  18. Sitar-Taut VA, Zdrenghea D, Pop D, Sitar-Taut DA. Using machine learning algorithms in cardiovascular disease risk evaluation. Journal of Applied Computer Science & Mathematics. 2009; 5(3):29-32.
  19. Tu MC, Shin D, Shin D. Effective diagnosis of heart disease through bagging approach. Paper presented at: The 2nd International Conference on Biomedical Engineering and Informatics. 2015 March 30-31; Penang, Malaysia.
  20. Chaurasia V, Pal S. Early prediction of heart diseases using data mining techniques. Caribbean Journal of Science and Technology. 2013; 1:208-17.
  21. Mahdi N, Behrouz M, Amir K. Dynamic recommendation: Disease prediction and prevention using recommender system. International Journal of Basic Science in Medicine. 2016; 1(1):13-7.
  22. Rajeswari K, Vaithiyanathan V, Neelakantan, TR. Feature selection in ischemic heart disease identification using feed forward neural networks. International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012). Procedia Engineering. 2012; 41:1818-23.
آمار
تعداد مشاهده مقاله: 3,864
تعداد دریافت فایل اصل مقاله: 726


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب