Cover Image
QR Code

Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model.

The World Health Organization (WHO) estimated that in 2016, 1.6 million deaths caused were due to diabetes. Precise and on-time diagnosis of type-II diabetes is crucial to reduce the risk of various diseases such as heart disease, stroke, kidney disease, diabetic retinopathy, diabetic neuropathy, an...

Full description

Published in:Medical & Biological Engineering & Computing Vol. 59; no. 4; pp. 841 - 868
Main Authors: Kanimozhi, N., Singaravel, G.
Format: Journal Article
Published: Springer Nature Apr2021
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149884709&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 149884709
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        01400118
        PO0
      jtl: Medical & Biological Engineering & Computing
      issn: 01400118
      maglogo: N
    pubinfo:
      dt: Apr2021
      vid: 59
      iid: 4
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        149884709
        149349642
        149884709
        NLM33738640
        10.1007/s11517-021-02333-x
        NLM33738640
        149884709
      ppf: 841
      ppct: 27
      formats:
        fmt:
          @attributes:
            type: P
      tig:
        atl: Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model.
      aug:
        au:
          Kanimozhi, N.
          Singaravel, G.
        affil: Department of Computer Science and Engineering, GKM College of Engineering and Technology, Chennai, India
      sug:
        subj:
          Diabetes Mellitus, Type 2 Diagnosis
          Particle Swarm Optimization
          Extreme Learning Machines
          Algorithms
      ab: The World Health Organization (WHO) estimated that in 2016, 1.6 million deaths caused were due to diabetes. Precise and on-time diagnosis of type-II diabetes is crucial to reduce the risk of various diseases such as heart disease, stroke, kidney disease, diabetic retinopathy, diabetic neuropathy, and macrovascular problems. The non-invasive methods like machine learning are reliable and efficient in classifying the people subjected to type-II diabetics risk and healthy people into two different categories. This present study aims to develop a stacking-based integrated kernel extreme learning machine (KELM) model for identifying the risk of type-II diabetic patients based on the follow-up time on the diabetes research center dataset. The Pima Indian Diabetic Dataset (PIDD) and a Diabetic Research Center dataset are used in this study. A min-max normalization is used to preprocess the noisy datasets. The Hybrid Particle Swarm Optimization-Artificial Fish Swarm Optimization (HAFPSO) algorithm used satisfies the multi-objective problem by increasing the Classification Accuracy (CA) and decreasing the kernel complexity of the optimal learners (NBC) selected. At last, the model is integrated by utilizing the KELM as a meta-classifier which combines the predictions of the twenty Base Learners as a whole. The proposed classification method helps the clinicians to predict the patients who are at a high risk of type-II diabetes in the future with the highest accuracy of 98.5%. The proposed method is tested with different measures such as accuracy, sensitivity, specificity, Mathews Correlation Coefficient, and Kappa Statistics are calculated. The results obtained show that the KELM-HAFPSO approach is a promising new tool for identifying type-II diabetes.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N