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.