A Hybrid Approach for Depression Classification: Random Forest-ANN Ensemble on Motor Activity Signals

TL;DR

This paper introduces a hybrid Random Forest-ANN ensemble algorithm that analyzes wearable sensor data to classify depression with 80% accuracy, advancing mental health diagnostics.

Contribution

The study presents a novel hybrid ensemble method combining Random Forest and Neural Network tailored for depression classification using sensor data.

Findings

Achieved 80% accuracy in classifying depression.

Effectively distinguished between unipolar, bipolar, and healthy controls.

Demonstrated potential for reliable mental health assessment using wearable sensors.

Abstract

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.