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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Decision Tree Versus Linear Support Vector Machine Classifier in the Screening of Medial Speech Sounds: A Quest for a

Emilian Erman Mahmut1, Stelian Nicola1, Vasile Stoicu-Tivadar1

  • 1Politehnica University Timisoara, Romania Department of Automation and Applied Informatics.

Studies in Health Technology and Informatics
|October 23, 2023
PubMed
Summary

This study enhanced a Speech Sound Disorder (SSD) screening algorithm using two classifiers. The Decision Tree classifier achieved 100% accuracy, outperforming the Linear Support Vector Machine (SVM) model.

Keywords:
Decision Tree ClassifierLinear SVMSpeech Sound Disorders

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Area of Science:

  • Computational Linguistics
  • Speech Pathology
  • Machine Learning

Background:

  • Speech Sound Disorder (SSD) affects child development.
  • Accurate screening is crucial for early intervention.
  • Existing algorithms require refinement for improved classification.

Purpose of the Study:

  • To evaluate the performance of two machine learning classifiers for SSD screening.
  • To compare a Classification and Regression Tree (CART) model against a Linear Support Vector Machine (SVM).
  • To identify the optimal classifier for an SSD screening algorithm.

Main Methods:

  • Extracted 10 audio features for medial speech sounds.
  • Incorporated a Speech Language Pathologist (SLP) validation feature.
  • Trained and tested CART and Linear SVM models on speech sample data.

Main Results:

  • The Decision Tree classifier achieved 100% accuracy.
  • The Linear SVM classifier achieved 98.2% accuracy.
  • Both models demonstrated high performance on a 30% test data subset.

Conclusions:

  • The Decision Tree classifier shows superior performance for SSD screening.
  • The developed algorithm shows promise for accurate and efficient SSD identification.
  • Further research is warranted to refine the algorithm and explore its clinical application.