NCT06160674

Vowel Segmentation for Classification of Chronic Obstructive Pulmonary Disease Using Machine Learning

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 68 (actual)
• Sponsor: Blekinge Institute of Technology · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

This work aims to evaluate whether the segmentation of vowel recordings collected from patients diagnosed with COPD and healthy control groups can increase the classification precision of machine learning techniques.

Detailed description

Voice data and sociodemographic data on gender and age will be collected through the "VoiceDiganostic" application from the company Voice Diagnostic. Collected vowel recordings will be segmented and tested to determine whether some segments contain more information for the discrimination of COPD from healthy control groups. Each segment will be transformed into mathematical vocal measures called voice features. A dataset consisting of voice features in conjunction with demographics and health data will be constructed for each segment which in turn will be evaluated for classification performance using several machine learning algorithms. Descriptive statistical analysis will be held on attributes containing information on input data and gained outcomes from ML algorithms. The achieved results will be presented in the form of summary tables and graphs.

Conditions

• Chronic Obstructive Pulmonary Disease

Interventions

Type	Name	Description
OTHER	COPD	A vowel segmentation data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques on different segments of a vowel recording.

Timeline

• Start date: 2023-11-28
• Primary completion: 2024-10-30
• Completion: 2024-11-30
• First posted: 2023-12-07
• Last updated: 2024-11-25

Locations

1 site across 1 country: Sweden