Creating and Assessing a Voice Dataset for Automated Classification of Chronic Obstructive Pulmonary Disease

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

72 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-12-16

Study Completion Date

2024-10-30

Brief Summary

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This work aims to evaluate whether voice recordings collected from patients diagnosed with COPD and healthy control groups can be used to detect the disease using machine learning techniques.

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Detailed Description

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Voice data and sociodemographic data on gender and age will be collected through the "VoiceDiganostic" application from the company Voice Diagnostic, which allows one to participate without location dependency. Participants with a diagnosis will be marked as the COPD group, and others will be marked as the healthy control group. Private information such as known comorbidities, personal security numbers, health parameters and communication information will be separately noticed in a participation table for each group.

The collected data 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 further usage as an input to ML techniques.

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

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Chronic Obstructive Pulmonary Disease

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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COPD

Participants with clinically diagnosed Chronic obstructive pulmonary disease. Total 34 recruitment, 18 Female, 16 Male

COPD

Intervention Type OTHER

A data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques.

HC

Participants without Chronic obstructive pulmonary disease diagnosis. Total 38 recruitment, 20 Female, 18 Male

COPD

Intervention Type OTHER

A data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques.

Interventions

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COPD

A data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques.

Intervention Type OTHER