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

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

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.

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.

Inclusion Criteria:

• being 18 years old and older.

Exclusion Criteria:

• being under 18 years old.