Overview
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.
Description
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.
Eligibility
Inclusion Criteria:
- being 18 years old and older.
Exclusion Criteria:
- being under 18 years old.