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

 
 
 
  • Abstract
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  • 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


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




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