A Hybrid Approach for Depression Classification: Random Forest-ANN
  Ensemble on Motor Activity Signals
Anket Patil Dhairya Shah Abhishek Shah Mokshit Gala
Regarding the rising number of people suffering from mental health illnesses
in today's society, the importance of mental health cannot be overstated.
Wearable sensors, which are increasingly widely available, provide a potential
way to track and comprehend mental health issues. These gadgets not only
monitor everyday activities but also continuously record vital signs like heart
rate, perhaps providing information on a person's mental state. Recent research
has used these sensors in conjunction with machine learning methods to identify
patterns relating to different mental health conditions, highlighting the
immense potential of this data beyond simple activity monitoring. In this
research, we present a novel algorithm called the Hybrid Random forest - Neural
network that has been tailored to evaluate sensor data from depressed patients.
Our method has a noteworthy accuracy of 80\% when evaluated on a special
dataset that included both unipolar and bipolar depressive patients as well as
healthy controls. The findings highlight the algorithm's potential for reliably
determining a person's depression condition using sensor data, making a
substantial contribution to the area of mental health diagnostics.