Optimization of Depression Level Detection Based on Noninvasive Parameters with Artificial Neural Network

Abstract

We optimized the measurement of depression levels by developing an instrument that processes four physical parameters non-invasively with an Artificial Neural Network (ANN). High-accuracy detection instruments help establish a diagnosis to realize early and appropriate prevention and treatment of depression. Depression among students tends to increase, which has an impact on reducing productivity and even attempting suicide. It can reduce the quality and quantity of the productive age generation. The selection of non-invasive physical parameters, namely heart rate, hours of sleep, respiratory rate, and blood oxygen, considers practical aspects for the examiner and the subject examined. This research uses an exploratory method to develop an ANN-based tool for detecting student depression levels. The ANN-based Depression Detector (dANN) input layer harvests data from measurements of the four parameters. Then, the ANN hidden layer processes and studies the data in depth to present five levels of depression in the output layer. Learning results using Loss Graphs show that dANN reduces prediction errors or training iterations at Epochs greater than 130 or offers high accuracy after reaching Epoch 130. DANN prioritized high accuracy in practical measurements. dANN has the potential to be further developed by integrating psychological parameter data, considering that depression is multifactorial. The result of classification reported a precision of 1.0. This felicitating value presents the success of learning and training on dANN. Furthermore, dANN will be built on a web basis to improve performance by implementing an expert system.