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.本数据集聚焦于通过将数据排布为二维（2D）参数形式，优化人工神经网络（Artificial Neural Networks，ANN）的输入结构。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。这些数据将作为人工神经网络的输入，经训练后的模型可基于数据中挖掘的模式预测抑郁程度。通过此种数据结构化方式，模型在识别抑郁严重程度时的适配性与有效性得以提升，进而改善心理健康评估的准确率。