A Novel Hyper-Active Algorithm to Estimate Missing Microarray Attributes


  •  Baydaa Al-Hamadani    
  •  Thikra Shubita    

Abstract

Classification the Microarray dataset is a powerful method used in clinical and biomedical studies, to estimate and diagnose some diseases like (cancer, non-cancer) depending on Gene expression. To be full beneficial, the gene expression dataset should be complete; i.e. with no missing data. Several approaches were proposed to deal with these missing values. In this paper, a robust algorithm is proposed based on the optimal fitting analysis to estimate the missing values in the microarray data. Then, the complete dataset is used to estimate the probability of lung cancer occurrence based on stochastic algorithm and support vector machine (SVM). The designed algorithm has been applied on different types of datasets varies from complete to different percent of missing data. Comparisons have been done with different other algorithms from the accuracy and error rates perspectives. The experimental results indicate that the proposed algorithm surpass other tested methods.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

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