In the healthcare industry, researchers have been developing machine learning models to automate diagnosing patients with respiratory illnesses based on their breathing patterns. However, these models do not consider the demographic biases, particularly sex bias, that often occur when models are trained with a skewed patient dataset. Hence, it is essential in such an important industry to reduce this bias so that models can make fair diagnoses. In this work, we examine the bias in models used to detect breathing patterns of two major respiratory diseases, i.e., chronic obstructive pulmonary disease (COPD) and COVID-19. Using decision tree models trained with audio recordings of breathing patterns obtained from two open-source datasets consisting of 29 COPD and 680 COVID-19-positive patients, we analyze the effect of sex bias on the models. With a threshold optimizer and two constraints (demographic parity and equalized odds) to mitigate the bias, we witness 81.43% (demographic parity difference) and 71.81% (equalized odds difference) improvements. These findings are statistically significant.

翻译：在医疗健康领域，研究者们一直致力于开发机器学习模型，以基于患者的呼吸模式实现呼吸系统疾病的自动化诊断。然而，这些模型通常未考虑人口统计学偏差，尤其是当模型使用存在偏斜的患者数据集进行训练时经常出现的性别偏见。因此，在这一重要行业中，减少此类偏差至关重要，以确保模型能够做出公平的诊断。在本研究中，我们探究了用于检测两种主要呼吸系统疾病——慢性阻塞性肺疾病（COPD）与COVID-19——呼吸模式的模型中所存在的偏见。我们利用从两个开源数据集（包含29名COPD患者与680名COVID-19阳性患者）获取的呼吸模式音频记录训练决策树模型，并分析了性别偏见对模型的影响。通过采用阈值优化器及两项约束条件（人口统计均等与机会均等）来缓解偏见，我们观察到模型性能分别提升了81.43%（人口统计均等差异）与71.81%（机会均等差异）。这些发现具有统计学显著性。