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Related Experiment Video

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Automatic illness prediction system through speech.

Husam Ali Abdulmohsin1, Belal Al-Khateeb2, Samer Sami Hasan1

  • 1Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq.

Computers & Electrical Engineering : an International Journal
|July 26, 2022
PubMed
Summary
This summary is machine-generated.

Automated illness prediction from speech shows promise, achieving 94.55% accuracy by categorizing diseases based on pain and psychological state. This voice-based diagnostic approach offers a new avenue for remote healthcare.

Keywords:
ADPS, Automated Disease Prediction SystemAutomatic disease predictionCPU, Central Processing UnitForward-backward filterGA, Genetic AlgorithmGB, Giga ByteGMM, Gaussian Mixture ModelMFCC, Mel Frequency Cepstral Co-efficientMedical speech transcription and intent datasetMel frequency Cepstral coefficientNN, Neural NetworkNeural networkRAM, Random Access MemoryRSM, Response Service MethodologySCV, Spectral Centroid VariabilitySVM, Support Vector MachineSpectral centroid variability

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Area of Science:

  • Artificial Intelligence in Medicine
  • Speech Signal Processing
  • Computational Linguistics

Background:

  • The COVID-19 pandemic accelerated the demand for remote healthcare solutions, including online automated illness prediction systems (ADPS).
  • Voice analysis for medical diagnosis is an emerging field with significant potential for non-invasive patient assessment.
  • Existing research highlights the need for robust methods to handle noisy online voice data and extract relevant medical indicators.

Purpose of the Study:

  • To develop and implement an automated illness prediction system utilizing speech characteristics.
  • To categorize diseases into groups (painful, light pain, psychological pain) for improved diagnostic accuracy.
  • To evaluate the effectiveness of various speech features and machine learning models for illness classification.

Main Methods:

  • Utilized the "speech, transcription, and intent" medical dataset.
  • Extracted speech features including smoothness, Mel-frequency cepstral coefficients (MFCC), and spectral centroid variation (SCV).
  • Applied noise reduction using a forward-backward filter, a hybrid feature selection combining genetic algorithms (GA) and neural networks (NN), and classification with Support Vector Machines (SVM), neural networks, and Gaussian Mixture Models (GMM).

Main Results:

  • Achieved a maximum illness classification accuracy of 94.55% using Support Vector Machines (SVM).
  • Demonstrated that grouping illnesses by pain level and psychological state significantly improved diagnostic performance compared to individual disease classification.
  • Validated the representational capacity of smoothness, MFCC, and SCV features for medical situations.

Conclusions:

  • Automated illness prediction from speech is feasible, particularly when diseases are categorized based on symptom severity and psychological impact.
  • The developed hybrid feature selection and classification framework, especially with SVM, shows high potential for accurate voice-based health assessment.
  • This approach offers a promising direction for the future of remote and automated medical diagnosis.