Performance metrics for biomarker models
Our objective was to build a balanced model capable of effectively distinguishing samples from participants in the SR group versus the CON group. Given the early stage of this research, we aimed for a model that equally prioritized sensitivity and specificity, which helps minimize bias from unbalanced sample sizes. A commonly used performance metric in this context is overall accuracy, which calculates the proportion of correctly classified samples. However, when study group sample sizes are unbalanced, this method can lead to bias in favor of the larger group. In our study, the SR group contained more samples (n = 34) than the CON group (n = 21). To prevent this issue, we used the G-Mean indicator, a frequently used performance metric in similar situations