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

Chronic Obstructive Pulmonary Disease Clinical Trial

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

Status Active, not recruiting

Clinical Trial 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Chronic Obstructive Pulmonary Disease
• Lung Diseases
• Lung Diseases, Obstructive
• Pulmonary Disease, Chronic Obstructive

• NCT number: NCT06160674
• Study type: Observational [Patient Registry]
• Source: Blekinge Institute of Technology
• Status: Active, not recruiting
• Start date: November 28, 2023
• Completion date: August 30, 2024