2D Data for Training Artificial Neural Networks in Measuring Depression Levels

This dataset focuses on optimizing input structures for Artificial Neural Networks (ANN) by arranging data in two-dimensional (2D) parameters. The approach involves collecting and organizing a combination of physical parameters (body temperature, heart rate, oxygen saturation, and sleep duration) and psychological parameters (Perceived Stress Scale) from individuals to create 2D data. These data serve as input for the ANN, which is trained to predict depression levels based on patterns detected in the data. By structuring the data in this manner, the model becomes more adaptive and effective in identifying the severity of depression, leading to improved accuracy in mental health assessments.本数据集旨在通过将参数整理为二维（2D）结构，优化人工神经网络（Artificial Neural Networks，ANN）的输入架构。该方法通过采集并整合个体的生理参数（体温、心率、血氧饱和度与睡眠时长）与心理参数（感知压力量表（Perceived Stress Scale）），构建二维数据集。此类数据将作为人工神经网络的输入，该网络经训练后可基于数据中挖掘的模式预测抑郁程度。通过此种数据组织方式，模型在识别抑郁严重程度时具备更强的适应性与有效性，进而提升心理健康评估的准确率。