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Related Experiment Video

Updated: Aug 11, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction.

Tengku Mazlin Tengku Ab Hamid1, Roselina Sallehuddin1, Zuriahati Mohd Yunos1

  • 1Department of Computer Science, Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia.

Neural Computing & Applications
|February 7, 2023
PubMed
Summary
This summary is machine-generated.

Early detection of hearing disorders is crucial. This study presents a novel method combining ensemble feature selection and optimized machine learning to improve hearing loss prediction accuracy.

Keywords:
Feature selectionFilters algorithmParticle swarm optimizationSupport vector machine

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Area of Science:

  • Audiology
  • Machine Learning
  • Data Science

Background:

  • Early intervention for hearing disorders is vital for communication development.
  • Increasing data complexity challenges accurate audiometry and treatment decisions.
  • Independent feature selection and classification degrade prediction accuracy.

Purpose of the Study:

  • To address challenges in hearing disorder prediction accuracy caused by irrelevant features and suboptimal classifier parameters.
  • To develop an integrated approach for feature selection and classification in audiology.
  • To enhance the performance of hearing loss prediction systems.

Main Methods:

  • An ensemble feature selection method using Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF).
  • Simultaneous optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) parameters.
  • Application of the proposed method to a standard Audiology dataset.

Main Results:

  • The proposed method achieved 96.50% accuracy on the Audiology dataset.
  • Demonstrated superior performance compared to classical Support Vector Machine (SVM).
  • Indicated effectiveness in handling high-dimensional data for hearing disorder prediction.

Conclusions:

  • The integrated ensemble feature selection and PSO-SVM optimization effectively improves hearing disorder prediction accuracy.
  • The proposed method mitigates issues related to irrelevant features and improper classifier parameters.
  • This approach offers a robust solution for audiology systems dealing with complex datasets.