Depression is a common yet serious mental disorder that affects millions of
U.S. high schoolers every year. Still, accurate diagnosis and early detection
remain significant challenges. In the field of public health, research shows
that neural networks produce promising results in identifying other diseases
such as cancer and HIV. This study proposes a similar approach, utilizing
machine learning (ML) and artificial neural network (ANN) models to classify
depression in a student. Additionally, the study highlights the differences in
relevant factors for race subgroups and advocates the need for more extensive
and diverse datasets. The models train on nationwide Youth Risk Behavior
Surveillance System (YRBSS) survey data, in which the most relevant factors of
depression are found with statistical analysis. The survey data is a structured
dataset with 15000 entries including three race subsets each consisting of 900
entries. For classification, the research problem is modeled as a supervised
learning binary classification problem. Factors relevant to depression for
different racial subgroups are also identified. The ML and ANN models are
trained on the entire dataset followed by different race subsets to classify
whether an individual has depression. The ANN model achieves the highest F1
score of 82.90% while the best-performing machine learning model, support
vector machines (SVM), achieves a score of 81.90%. This study reveals that
different parameters are more valuable for modeling depression across diverse
racial groups and furthers research regarding American youth depression.

该研究使用神经网络和机器学习模型对学生的抑郁症进行分类，并指出不同种族亚组的相关因素的差异，强调了更多广泛和多样化数据集的需求。