Creating and Assessing a Voice Dataset for Automated Classification of Chronic Obstructive Pulmonary Disease
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 72
- 试验地点
- 1
- 主要终点
- Accuracy
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •being 18 years old and older.
排除标准
- •being under 18 years old.
结局指标
主要结局
Accuracy
时间窗: Week 51
Binary detection performance of the ML algorithm
Input data importance scale
时间窗: Week 51
Features used as input data will be ranked from most important to less important one.
次要结局
未报告次要终点
研究者
Johan Sanmartin Berglund
Professor, MD, PhD
Blekinge Institute of Technology