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Diabetes disease prediction system using HNB classifier based on discretization method.

Bassam Abdo Al-Hameli1, AbdulRahman A Alsewari2, Shadi S Basurra2

  • 1Centre for Software Development & Integrated Computing, Faculty of Computing, Universiti Malaysia Pahang, Pahang 26600, Malaysia.

Journal of Integrative Bioinformatics
|February 22, 2023
PubMed
Summary
This summary is machine-generated.

Early diabetes diagnosis is crucial for patient health. This study shows Hidden Naïve Bayes (HNB) with discretization achieves 82% accuracy in diabetes prediction, improving early detection rates.

Keywords:
HNBPima datasetclassificationdata miningdiscretization

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Data Mining

Background:

  • Early diabetes diagnosis is vital for effective disease management and preventing complications.
  • Data mining techniques offer high confidence in diabetes detection, distinguishing it from similar chronic diseases.
  • Hidden Naïve Bayes (HNB) is a classification algorithm used in data mining, building upon traditional Naïve Bayes.

Purpose of the Study:

  • To evaluate the effectiveness of the Hidden Naïve Bayes (HNB) classifier for early diabetes detection.
  • To assess the impact of discretization methods on the performance of the HNB classifier in diabetes prediction.

Main Methods:

  • Utilized the Pima Indian Diabetes (PID) dataset for the study.
  • Applied the Hidden Naïve Bayes (HNB) classification algorithm.
  • Incorporated a discretization method to preprocess the data for the HNB classifier.

Main Results:

  • The HNB classifier achieved a prediction accuracy of 82% on the PID dataset.
  • The application of a discretization method significantly enhanced the performance and accuracy of the HNB classifier.

Conclusions:

  • Discretization is an effective strategy for improving the accuracy of the HNB classifier in diabetes prediction.
  • The HNB algorithm, enhanced by discretization, shows promise for reliable early diabetes detection.