A Hybrid Approach for Depression Classification: Random Forest-ANN Ensemble on Motor Activity Signals
This work addresses mental health diagnostics for patients with depression, but it is incremental as it combines existing methods on new data.
The paper tackled depression classification by developing a hybrid Random Forest-ANN ensemble method for analyzing motor activity signals from wearable sensors, achieving an accuracy of 80% on a dataset including unipolar and bipolar depressive patients and healthy controls.
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.