A fuzzy analytic hierarchy attribute weighting and deep learning for improving CHD prediction of optimized semi parametric extended dynamic bayesian network

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Several Data mining techniques have been developed to enhance the prediction accuracy and analyze several events in Coronary Heart Disease (CHD).  One among them was Extended Dynamic Bayesian Network (EDBN) which integrates   temporal abstractions with DBN. Then EDBN was extended as Optimized Semi parametric Extended Dynamic Bayesian Network (OSEDBN) to handle Complex temporal abstractions in irregular interval time series data. The deep learning network is generated the various time points in the next level to improve the analysis and prediction of CHD. In this paper, Optimized Semi parametric Extended Deep Dynamic Bayesian Network (OSEDDBN) is proposed by integrating deep learning architecture with OSEDBN to improve the ability of extracting more important data and support complex structures from various types of input sources. Additionally the Fuzzy Analytic Hierarchy Process (FAHP) approach is used to compute the global weights for the attributes based on their individual contribution. The global weights of the attributes obtained by FAHP are utilized for training OSEDDBN to further improve the prediction of Coronary Heart Disease (CHD) risks. The performance of EDBN, OSEDBN, OSEDDBN, and OSEDDBN-FAHP are evaluated in terms of Precision, Recall and F-Measure.

  • Keywords

    Coronary Heart Disease (CHD), Fuzzy Analytic Hierarchy Process (FAHP), Extended Dynamic Bayesian Network (EDBN) and Deep Dynamic Bayesian Network (DDBN)

  • References

      [1] Methaila A, Kansal P, Arya H & Kumar P, “Early heart disease prediction using data mining techniques”, Computer Science & Information Technology Journal, (2014), pp.53-59.

      [2] Karaolis MA, Moutiris JA, Hadjipanayi D & Pattichis CS, “Assessment of the risk factors of coronary heart events based on data mining with decision trees”, IEEE Transactions on information technology in biomedicine, Vol.14, No.3, (2010), pp.559-566.

      [3] Kim J, Lee J & Lee Y, “Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree”, Healthcare informatics research, Vol.21, No.3, (2015), pp.167-174.

      [4] Chaurasia V & Pal S, “Early prediction of heart diseases using data mining techniques”, Caribbean Journal of Science and Technology, Vol.1, (2013), pp.208-217.

      [5] Rajkumar A & Reena GS, “Diagnosis of heart disease using datamining algorithm”, Global journal of computer science and technology, Vol.10, No.10, (2010), pp.38-43.

      [6] Van Gerven MA, Taal BG & Lucas PJ, “Dynamic Bayesian networks as prognostic models for clinical patient management”, Journal of biomedical informatics, Vol.41, No.4, (2008), pp.515-529.

      [7] Orphanou K, Keravnou E & Moutiris J, “Integration of Temporal Abstraction and Dynamic Bayesian Networks in Clinical Systems” A preliminary approach”, OASIcs-OpenAccess Series in Informatics, Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, (2012).

      [8] Sawa MSA, Naba A & Dachlan HS, “Bayesian Network Expert System for Early Diagnosis of Heart Diseases”, Journal EECCIS, Vol.7, No.2, (2014), pp.171-178.

      [9] Pjanic M, Miller CL, Wirka R, Kim JB, DiRenzo DM & Quertermous T, “Genetics and genomics of coronary artery disease”, Current cardiology reports, Vol.18, No.10, (2016).

      [10] Hsieh NC, Hung LP, Shih CC, Keh HC & Chan CH, “Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques”, Journal of medical systems, Vol.36, No.3, (2012), pp.1809-1820.

      [11] Orphanou K, Stassopoulou A & Keravnou E, “Risk assessment for primary coronary heart disease event using dynamic Bayesian networks”, Conference on Artificial Intelligence in Medicine in Europe, (2015), 161-165.

      [12] Surendar, A., M. Kavitha, and V. Saravanakumar. "Proactive model based testing and evaluation for component-based systems." International Journal of Engineering & Technology 8.1.1 (2018): 74-77.

      [13] Adeli A & Neshat M, “A fuzzy expert system for heart disease diagnosis”, Proceedings of International Multi Conference of Engineers and Computer Scientists, (2010).

      [14] Feng Y, Wang Y, Guo F & Xu H, “Applications of data mining methods in the integrative medical studies of coronary heart disease: progress and prospect”, Evidence-Based Complementary and Alternative Medicine, (2014).

      [15] Khan IY, Zope PH & Suralkar SR, “Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart disease”, International Journal of Engineering Science and Innovative Technology (IJESIT), Vol.2, No.2, (2013), pp.210-217.

      [16] Saaty TL, “How to make a decision: The analytic hierarchy process”, European Journal of Operational Research, Vol.48, No.1, (1990), pp.9-26.




Article ID: 9274
DOI: 10.14419/ijet.v7i1.1.9274

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.