Quarterly Publication

Document Type : Original Article


1 Department of Computer Engineering, Technical and Vocational University, Iran.

2 Department of Computer, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.


Coronary artery heart failure is the leading cause of mortality among other cardiac diseases. In most of the cases, angiography is a reliable method for the diagnosis and treatment of cardiovascular diseases. However, it is a costly approach associated with various complications. The significant increase in the prevalence of cardiovascular diseases and the subsequent complications and treatment costs have urged researchers to plan for the better examination, prevention, early detection, and effective treatment of these conditions. The present study aimed to determine the patterns of cardiovascular diseases using integrated classification techniques for analyzing the data of internal medicine patients who are at the risk of heart failure with 451 samples and 13 characteristics. Selecting characteristics and evaluating the influential factors are essential to the development of classifiers and increasing their accuracy. Therefore, we investigated the influential factors of the Gini index. In the classification phase, basic techniques were used, including a decision tree, a neural network, and different cumulative techniques such as gradient boosting, random forest, and the novel deep learning method. A comparison revealed that deep learning with the accuracy of 95.33%, disease class accuracy of 95.77%, and health class accuracy of 94.74% could enhance the presentation and results of the neural network. Out findings confirmed that cumulative methods and selecting influential factors are essential to increasing the accuracy of the diagnostic systems for heart failure. Furthermore, the reported practical tree rules emphasized the use of analytical methods to extract knowledge.


  1. Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A. (2017). Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm. Computer methods and programs in biomedicine141, 19-26. https://doi.org/10.1016/j.cmpb.2017.01.004
  2. Maji, S., & Arora, S. (2019). Decision tree algorithms for prediction of heart disease. In Information and communication technology for competitive strategies(pp. 447-454). Springer, Singapore. https://doi.org/10.1007/978-981-13-0586-3_45
  3. Jalali, S. M. J., Karimi, M., Khosravi, A., & Nahavandi, S. (2019, October). An efficient neuroevolution approach for heart disease detection. 2019 IEEE international conference on systems, man and cybernetics (SMC)(pp. 3771-3776). IEEE. DOI: 1109/SMC.2019.8913997
  4. Toghraee, M. (2019). Calculation of mean data on gini relationship by data mining method. NatureCiiT international journal of data mining and knowledge engineering, 11(8), 129-133.
  5. Javeed, A., Zhou, S., Yongjian, L., Qasim, I., Noor, A., & Nour, R. (2019). An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection. IEEE access7, 180235-180243. DOI: 1109/ACCESS.2019.2952107
  6. Ala'raj, M., & Abbod, M. (2015, September). A systematic credit scoring model based on heterogeneous classifier ensembles. 2015 international symposium on innovations in intelligent systems and applications (INISTA)(pp. 1-7). IEEE. DOI: 1109/INISTA.2015.7276736
  7. Masmoudi, K., Abid, L., & Masmoudi, A. (2019). Credit risk modeling using Bayesian network with a latent variable. Expert systems with applications127, 157-166. https://doi.org/10.1016/j.eswa.2019.03.014
  8. Enriko, I. K. A. (2019, June). Comparative study of heart disease diagnosis using top ten data mining classification algorithms. Proceedings of the 5th international conference on frontiers of educational technologies(pp. 159-164). https://doi.org/10.1145/3338188.3338220
  9. Rokach, L. (2010). Ensemble-based classifiers. Artificial intelligence review33(1), 1-39. https://doi.org/10.1007/s10462-009-9124-7
  10. Wirth, R., & Hipp, J. (2000). CRISP-DM: towards a standard process model for data mining. Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining(Vol. 1, pp. 29-39). http://www.cs.unibo.it/~danilo.montesi/CBD/Beatriz/
  11. Dangare, Ch. S., & Apte, S. S. (2012). A data mining approach for prediction of heart disease using neural networks. International journal of computer engineering and technology (IJCET), 3(3), 30-40.
  12. Rajasekaran, C., Jayanthi, K. B., Sudha, S., & Kuchelar, R. (2019, July). Automated diagnosis of cardiovascular disease through measurement of Intima media thickness using deep neural networks. 41st annual international conference of the ieee engineering in medicine and biology society (embc) (pp. 6636-6639). IEEE.
  13. Baselli, G., Cerutti, S., Civardi, S., Lombardi, F., Malliani, A., Merri, M., ... & Rizzo, G. (1987). Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies. International journal of bio-medical computing, 20(1-2), 51-70.
  14. Joloudari, J. H., Saadatfar, H., GhasemiGol, M., Alizadehsani, R., Sani, Z. A., Hasanzadeh, F., ... & Mansor, Z. (2022). FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model. Available at arXiv:2202.04645
  15. Alizadehsani, R., Hosseini, M. J., Sani, Z. A., Ghandeharioun, A., & Boghrati, R. (2012). Diagnosis of coronary artery disease using cost-sensitive algorithms. IEEE 12th international conference on data mining workshops (pp. 9-16). IEEE.
  16. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  17. Amma, N. B. (2012, February). Cardiovascular disease prediction system using genetic algorithm and neural network. International conference on computing, communication and applications (pp. 1-5). IEEE.