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A hybrid of whale optimization and late acceptance hill climbing based imputation to enhance classification performance in electronic health records.

Electronic health records (EHR) are a major source of information in biomedical informatics. Yet, missing values are prominent characteristics of EHR. Prediction on dataset with missing values results in inaccurate inferences. Nearest neighbour imputation based on lazy learning approach is a proven...

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Published in:Journal of Biomedical Informatics Vol. 94; pp. 103190 - 103191
Main Authors: Nagarajan, Gayathri, Dhinesh Babu, L.D.
Format: Journal Article
Published: Academic Press Inc. Jun2019
Online Access:View this record in EBSCOhost
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      dt: Jun2019
      vid: 94
      pid: 735
      pub: Academic Press Inc.
      place: Burlington, Massachusetts
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        10.1016/j.jbi.2019.103190
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        atl: A hybrid of whale optimization and late acceptance hill climbing based imputation to enhance classification performance in electronic health records.
      aug:
        au:
          Nagarajan, Gayathri
          Dhinesh Babu, L.D.
        affil: School of Information Technology and Engineering, VIT University, India
      sug:
        subj:
          Algorithms
          Data Analysis, Statistical
          Data Collection
      ab: Electronic health records (EHR) are a major source of information in biomedical informatics. Yet, missing values are prominent characteristics of EHR. Prediction on dataset with missing values results in inaccurate inferences. Nearest neighbour imputation based on lazy learning approach is a proven technique for missing data imputation and is recognized as one among the top ten data mining algorithms due to its simplicity and understandability. But its performance is deteriorated due to the curse of dimensionality as unimportant features are likely to dominate. We address this problem by proposing a novel approach for feature weighting based on a hybrid of metaheuristic whale optimization algorithm (WOA) and local search late acceptance hill climbing algorithm (LAHCA) on nearest neighbour imputation method. Our proposed approach Metaheuristic and Local Search based Feature Weighted Nearest Neighbour Imputation (kNN+LAHCAWOA) also learns different k values for different test points. Our approach is tested on benchmark EHR datasets with three proven classifiers Support Vector Machines(SVM), Random forest(RF) and Deep neural networks(DNN). The results prove that kNN+LAHCAWOA is an effective imputation strategy and aids in improving the classification performance when compared with its competitor methods.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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