# Design and Implementation of Health and Risk Level Assessment for Socially Disadvantaged Patients
## 2 Related Work  
Depression is a chronic, recurrent disease that generally exhibits symptoms such as depressed mood, loss of interest and pleasure, and increased fatigue. Depression is accompanied by sadness, loss of interest or pleasure, guilt, low self-esteem, disturbed sleep or appetite, fatigue, and difficulty in concentrating. Since depression includes cases with mild symptoms, a more accurate expression is the major depression disorder, as defined by the American Psychiatric Association (APA) [4]. In this study, the \"Patient Health Questionnaire-9\", a tool developed by [5] and standardized by [6], was used to measure depression [4; 5; 6]. Anxiety is an emotional state that many people feel very commonly in modern society, where social relationships have become more complex and demands have increased. Therefore, it is a research topic in which psychologists studying psychological maladjustment and psychopathology have consistently shown great interest [7]. In the DSM-IV, anxiety disorders exhibit a complex form of various sub-symptoms and disorders and are subdivided according to the pattern in which pathological anxiety appears or the object and situation in which anxiety is felt [4]. In this study, \"Generalized Anxiety Disorder 7\", developed by [8], was used to measure anxiety. In Ref. [3], a study was conducted to build a watch-type device and server system to monitor and manage the health information of dementia patients in real time. We built a server system that collects physical activity information through a watch-type device and checks the patient's health information based on it. It was found that a relationship exists between smartphone sensor information and depression; it was confirmed through a study where GPS information collected from smartphones was related to the severity of depression. The researchers built a smart jewelry system that can be worn like an accessory to collect body information and built a system to manage depression based on this. The researchers collected heart rate, exercise time, exercise consumption, sleep time, and illumination information through the smart jewelry, and based on the collected information, a system was built to monitor depression. In addition, studies on depression relatedness based on sensor information such as smartphones and smart watches have been conducted, but research limitations remain due to the irregularity of sensor data, lack of collected variables, and difficulty in real-time analysis [9]. According to the research carried out in [2], community models for mental health promotion have been established. Suwon city established an approach by developing programs for each target group and operating intensive case management services through the ACT program. Suwon city's community model is characterized by its ability to be accessed by class through facilities such as the Suwon City Welfare Center. Hwaseong city carries out a support project for long-term hospitalized mental patients to return to and settle down in the community and supports various educational programs. The community model of Hwaseong city is characterized by the strengthening of public medical service infrastructure and policies centered on suicide prevention. Jeonju city operates a weekly rehabilitation program and provides counseling and treatment services to citizens who find it difficult to take care of their mental health due to social and personal conditions. The community model used in Jeonju city is characterized by promoting detailed projects centered on suicide prevention projects and children and youth projects. In [10], a machine learning model was utilized to detect depression in elderly people based on data extracted from personal wearable devices. The researchers built a model based on supervised learning, and three-axis accelerometer, temperature, BVP (blood volume pulse), HR (heart rate), and EDA (electrodermal activity) data were used as learning qualities. As a result of the experiment, a maximum F1 score of 0.971 was measured. Ref.