Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Pawlak, Z., Rough sets. International Journal of Computer and Information Sciences. 11(5):341–356, 1982.

    Article  Google Scholar 

  2. Bal, M., Rough sets theory as symbolic data mining method: an application on complete decision table. Information Science Letters. 2(1):35–47, 2013.

    Article  Google Scholar 

  3. Ali-Khashashneh, E. A. and Q. A. Al-Radaideh (2013) Evaluation of discernibility matrix based reduct computation techniques. 5th International Conference on Computer Science and Information Technology - IEEE, Amman. 76–81

  4. Yao, Y., and Zhao, Y., Discernibility matrix simplification for constructing attribute reducts. Information Sciences. 179(5):867–882, 2009.

    Article  Google Scholar 

  5. Wei, W., Liang, J., and Qian, Y., A comparative study of rough sets for hybrid data. Information Sciences. 190:1–16, 2012.

    Article  Google Scholar 

  6. Pawlak, Z., Rough set approach to knowledge-based decision support. European Journal of Operational Research. 99:48–57, 1997.

    Article  Google Scholar 

  7. Wang, J., Zhang, Q., Abdel-Rahman, H., and Abdel-Monem, M.I., A rough set approach to feature selection based on scatter search metaheuristic. Journal of Systems Science and Complexity. 27(1):157–168, 2014.

    Article  CAS  Google Scholar 

  8. Suguna, N., and Thanushkodi, K.G., An independent rough set approach hybrid with artificial bee colony algorithm for dimensionality reduction. American Journal of Applied Sciences. 8(3):261–266, 2011.

    Article  Google Scholar 

  9. Werbos, P (1974) Beyond regression new tools for prediction and analysis on the behavior sciences. PhD Thesis. Harvard University

  10. Rumelhart, D.E., Hinton, G.E., and Williams, R.J., Learning representations by back propagating errors. Nature. 323:533–536, 1986.

    Article  Google Scholar 

  11. Hayati, M., and Mohebi, Z., Application of artificial neural networks for temperature forecasting, World Academy of Science. Engineering and Technology. 1(4):654–658, 2007.

    Google Scholar 

  12. Ozyılmaz, L., and Yıldırım, T., Diagnosis of thyroid disease using artificial neural network methods. In: Proceedings of ICONIP’02 nineth international conference on neural information processing. Orchid Country Club, Singapore, pp. 2033–2036, 2002.

    Google Scholar 

  13. Hoshi, K., Kawakami, J., Kumagai, M., Kasahara, S., Nisimura, N., Nakamura, H., et al., An analysis of thyroid function diagnosis using Bayesian-type and SOM-type neural networks. Chemical and Pharmaceutical Bulletin. 53:1570–1574, 2005.

    Article  CAS  PubMed  Google Scholar 

  14. Delen, D., Walker, G., and Kadam, A., Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligence in Medicine Artificial Intelligence in Medicine. 34(2):113–127, 2005.

    Article  PubMed  Google Scholar 

  15. Ozsen, S., and Gunes, S., Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems. Expert Systems with Applications. 36(1):386–392, 2009.

    Article  Google Scholar 

  16. Polat, K., and Gunes, S., A new feature selection method on classification of medical datasets: kernel F-score feature selection. Expert Systems with Applications. 36(7):10367–10373, 2009.

    Article  Google Scholar 

  17. Kahramanli, H., and Allahverdi, N., Design of a hybrid system for the diabetes and heart diseases. Expert Systems with Applications. 35(1–2):82–89, 2008.

    Article  Google Scholar 

  18. Al-Shayea, Q.K., Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues. 8(2):150–154, 2011.

    Google Scholar 

  19. Er, O., Yumusak, N., and Temurtas, F., Chest diseases diagnosis using artificial neural networks. Expert Systems with Application. 37(12):7648–7655, 2010.

    Article  Google Scholar 

  20. Er, O., Tanrikulu, A.C., Abakay, A., and Temurtas, F., An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease. Computers & Electrical Engineering. 38(1):75–81, 2012.

    Article  Google Scholar 

  21. Temurtas, F., A comparative study on thyroid disease diagnosis using neural networks. Expert Systems with Applications. 36(1):944–949, 2009.

    Article  Google Scholar 

  22. Keles, A., and Keles, A., ESTDD: Expert system for thyroid diseases diagnosis. Expert Systems with Applications. 34(1):242–246, 2008.

    Article  Google Scholar 

  23. Sudha, M., Disease diagnosis using association rule mining based knowledge inference system. International Journal on Pharmacy and Technology. 8(3):16369–16379, 2017.

    Google Scholar 

  24. Liu, X., Wang, X., Su, Q., Zhang, M., Zhu, Y., Wang, Q., & Wang, Q (2017) A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method, Computational and mathematical methods in medicine.

  25. Olatunji, S.O., and Arif, H., Identification of Erythemato-Squamous skin diseases using extreme learning machine and artificial neural network. ICTACT Journal of Soft Computing. 4(1):627–632, 2013.

    Article  Google Scholar 

  26. Sharma, D.K., and Hota, H.S., Data mining techniques for prediction of different categories of dermatology diseases. Journal of Management Information and Decision Sciences. 16(2):103, 2013.

    Google Scholar 

  27. Danjuma K., Osofisan A O (2015) Evaluation of Predictive Data Mining Algorithms in Erythemato-Squamous Disease Diagnosis. arXiv preprint arXiv:1501.00607

  28. Masood A, Ali Al-Jumaily A (2013) Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. International journal of biomedical imaging.

  29. Bakpo F S., Kabari L G (2011) Diagnosing Skin Diseases Using an Artificial Neural Network. In Artificial Neural Networks-Methodological Advances and Biomedical Applications. Intech.

  30. Miller, R.A., Pople Jr., H.E., and Myers, J.D., Internist-I, an experimental computer-based diagnostic consultant for general internal medicine. New England Journal of Medicine. 307(8):468–476, 1982.

    Article  CAS  PubMed  Google Scholar 

  31. Swets, J.A., Measuring the accuracy of diagnostic systems. Science. 240(4857):1285–1293, 1988.

    Article  CAS  PubMed  Google Scholar 

  32. Huguet, J., Castineiras, M.J., and Fuentes-Arderiu, X., Diagnostic accuracy evaluation using ROC curve analysis. Scandinavian journal of clinical and laboratory investigation. 53(7):693–699, 1993.

    Article  CAS  PubMed  Google Scholar 

  33. Gil, D., Girela, J.L., De Juan, J., Gomez-Torres, M.J., and Johnsson, M., Predicting seminal quality with artificial intelligence methods. Expert Systems with Applications. 39(16):12564–12573, 2012.

    Article  Google Scholar 

  34. Krishnaiah, V., G. Narsimha, and N. Subhash Chandra (2015) Heart disease prediction system using data mining technique by fuzzy K-NN approach, Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India (CSI)

  35. Han, J., Kamber, M., and Pei, J., Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, Waltham, USA, 2011.

    Google Scholar 

  36. Skowron, A., J. Bazan., N. H. Son and J. Wroblewski (2005) RSES 2.2 user’s guide, Institute of Mathematics, Warsaw University, Warsaw, Poland

  37. Joshi, S., and Nair, M.K., Prediction of heart disease using classification based data mining techniques. Computational Intelligence in Data Mining- Springer. 2:503–511, 2015.

    Google Scholar 

  38. Kumar, S., and Sahoo, G., Classification of heart disease using Naive Bayes and genetic algorithm. In Computational Intelligence in Data Mining- Springer. 2:269–282, 2015.

    Google Scholar 

  39. Melillo, P., De Luca, N., Bracale, M., and Pecchia, L., Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE Journal of Biomedical and Health Informatics. 17(3):727–733, 2013.

    Article  PubMed  Google Scholar 

  40. Yadav, G., Kumar, Y., and Sahoo, G., Predication of Parkinson’s disease using data mining methods: a comparative analysis of tree, statistical, and support vector machine classifiers. Indian J. Med. Sci. 65(6):231, 2011.

    Article  PubMed  Google Scholar 

  41. Witten, I. H. and E. Frank (2005) Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco. P. 411

  42. Velickovski, F., Ceccaroni, L., Roca, J., Burgos, F., Galdiz, J.B., Marina, N., and Lluch-Ariet, M., Clinical Decision Support Systems (CDSS) for preventive management of COPD patients. Journal of translational medicine. 12(2):28, 2014.

    Google Scholar 

  43. Chaurasia, V., and Pal, S., Data Mining Techniques: To Predict and Resolve Breast Cancer Survivability. International Journal of Computer Science and Mobile Computing. 3:10–22, 2014.

    Google Scholar 

  44. Heydari, M., Teimouri, M., Heshmati, Z., and Alavinia, S.M., Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. International Journal of Diabetes in Developing Countries. 36(2):167–173, 2016.

    Article  Google Scholar 

  45. Patil, B.M., Joshi, R.C., and Toshniwal, D., Hybrid prediction model for type-2 diabetic patients. Expert systems with applications. 37(12):8102–8108, 2010.

    Article  Google Scholar 

Download references

Funding

No funding for this research work.

Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, India

    M. Sudha

Authors
  1. M. Sudha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Sudha.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sudha, M. Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice. J Med Syst 41, 178 (2017). https://doi.org/10.1007/s10916-017-0823-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-017-0823-3

Keywords

Search

Navigation