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

Chronic Obstructive Pulmonary Disease

Segmentation, Classification, COPD, Machine Learning

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.

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.

Sweden