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Implementation and Analysis of Classification Algorithms for Diabetes.

Dilip Kumar Choubey1, Sanchita Paul2, Smita Shandilya3

  • 1Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India.

Current Medical Imaging
|May 16, 2020
PubMed
Summary

This study introduces a novel diabetes classification technique using a genetic algorithm (GA) for attribute selection. GA enhances accuracy and reduces computation time for timely diabetes detection.

Keywords:
GAJ48graft DTLocalized diabetes datasetMLP NNNBsPIDDRBF NNclassificationdiabetesdiagnosisfeature selection.

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Computational Biology

Background:

  • Diabetes is a prevalent global health concern requiring accessible and sustainable management.
  • Effective and timely diagnosis is crucial for managing diabetes and preventing complications.

Purpose of the Study:

  • To develop and evaluate a novel classification technique for early and accurate diabetes detection.
  • To improve the efficiency and performance of diabetes diagnostic models.

Main Methods:

  • A localized diabetes dataset was compiled.
  • Several classification techniques (RBF NN, MLP NN, NBs, J48graft DT) were applied.
  • Genetic Algorithm (GA) was used for attribute selection, reducing 12 attributes to 6.

Main Results:

  • Comparative analysis showed that GA significantly enhances Receiver Operating Characteristic (ROC) and accuracy.
  • GA effectively removes insignificant attributes, reducing computational cost and time.

Conclusions:

  • The proposed strategy, utilizing GA for attribute selection, improves diabetes classification accuracy and efficiency.
  • This approach demonstrates potential for application in diagnosing other medical conditions.