Analysis of Depression Disorder with Motor Activity Time-Series Data Using Machine Learning and Deep Learning

The future of the healthcare system is being altered by new technology developments. Depression is a neurological condition that can cause significant emotional suffering. The way of brain working can change how much of an impact it has on the body. A person with depression typically has a low mood and may feel depressed or hopeless all the time. In response to loss or tragedy, depressive symptoms may appear briefly. However, if the symptoms persist for more than 2 weeks, it may indicate a significant depressive condition. The incidence of major depressive disorder is 350 million people worldwide (MDD). Historically, conventional techniques have been used to identify depression symptoms. Recently, research has started investigating the relationship among psychosocial characteristics, like quality-of-life scale, and mental health, that helps to identify and predict MDD earlier for better treatment. Finding the elements that contribute to depression may inspire new research and therapeutic approaches because depression is an illness that is increasingly posing a significant community health threat. In this work, we have provided comprehensive approaches to handle and examine the time series data and better understand the association between depressed aspects connected to physical activity in daily life using machine learning and deep learning techniques. There seem to be more direct links between various physical conditions and depression. These could end up being particularly interesting in terms of etiology. The two best examples are probably heart disease and stroke. The experimental results support the hypothesis that the change in the physical activity of daily life for a sequence of days is an indication of unipolar depression.