Abstract

For human the most fundamental requirement is having a healthy life, which is being difficult to maintain day to day as we are getting more progress in technological era. Among the possible reasons of unnatural death, heart disease based causes are showing very significant part. The diagnosis of heart diseases is a vital and intricate job. The recognition of heart disease from diverse features or signs is a multi-layered problem that is highly sensitive with respect diagnostic tests and establishing the relationship with multiple parameters is very difficult. In result decision is not free from false assumptions and is frequently accompanied by impulsive effects. This encourages developing a more reliable and cost effective knowledge based algorithmic approach to detect the heart disease. From engineering point of view, solution for detecting the presence of heart diseases is developed with the concept of artificial intelligence in data mining in this study. Feed forward architecture of neural network technology is taken as platform of computation to generate the intelligence in association with well established field of genetic algorithm (GA). A comparative performance has presented between both learning concepts with various different size of architecture.

Keywords:

Artificial intelligence , genetic algorithm , heart disease, neural network,

References

1. Alptekin, O. and A. Akan, 2010. Detection of some heart diseases by the analysis of ECG signals. Proceeding of the 18th IEEE Signal Processing and Communications Applications Conference (SIU), pp: 716-719.
   CrossRef    
2. Belloni, F., D. Della Giustina, S. Riboldi, M. Riva, E. Spoletini and L. Bertossi, 2007. Towards a computer-aided diagnosis by means of phonocardiogram signals. Proceeding of the IEEE International Symposium on Industrial Electronics (ISIE, 2007), pp: 2770-2775.
   CrossRef    
3. Hedeshi, N.G. and M.S. Abadeh, 2011. An ensemble PSO-based approach for diagnosis of coronary artery disease. Proceeding of the International Symposium on Artificial Intelligence and Signal Processing (AISP, 2011), pp: 77-82.
   
4. Kakadiaris, I.A., U. Kurkure, E.G. Mendizabal-Ruiz and M. Naghavi, 2009. Towards cardiovascular risk stratification using imaging data. Proceeding of the IEEE Annual International Conference on Engineering in Medicine and Biology Society (EMBC, 2009), pp: 1918-1921.
   CrossRef    
5. Ki-Hyeon, K. and C. Ho-Jin, 2007. Design of a clinical knowledge base for heart disease detection. Proceeding of the 7th IEEE International Conference on Computer and Information Technology (CIT, 2007), pp: 610-615.
   
6. Mahmood, A.M. and M.R. Kuppa, 2010. Early detection of clinical parameters in heart disease by improved decision tree algorithm. Proceeding of 2nd Vaagdevi International Information Technology for Real World Problems (VCON), pp: 24-29.
   
7. Oresko, J.J., Z. Jin, J. Cheng, S. Huang, Y. Sun et al., 2010. A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE T. Inf. Technol. B., 14(3): 734-740.
   CrossRef    PMid:20388600    
8. Palaniappan, S. and R. Awang, 2008. Intelligent heart disease prediction system using data mining techniques. Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA'08), pp: 108-115.
   CrossRef    
9. Sekar, B.D., C.D. Ming, S. Jun and Y.H. Xiang, 2012. Fused hierarchical neural networks for cardiovascular disease diagnosis. IEEE Sens. J., 12(3): 644-650.
   CrossRef    
10. Shi, J., B.D. Sekar, M.C. Dong and W.K. Lei, 2010. Fuzzy neural networks to detect cardiovascular diseases hierarchically. Proceeding of the 10th International Conference on Computer and Information Technology (CIT, 2010), pp: 703-708.
    CrossRef    
11. Sufi, F. and I. Khalil, 2011. Diagnosis of cardiovascular abnormalities from compressed ECG: A data mining-based approach. IEEE T. Inf. Technol. B., 15(1): 33-39.
    CrossRef    PMid:21097383    
12. World Health Organisation, 2011. Retrieved from: http://www.who.int.
    Direct Link

Competing interests

The authors have no competing interests.

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