# Design and Implementation of Health and Risk Level Assessment for Socially Disadvantaged Patients
## 2 Related Work
[11] analyzed the correlation between depressive symptoms and biometric information collected through a smart band, and a study was conducted on a model that could predict the degree of depression. Depression scale (PHQ-9), sleep time, sleep start time, and wake-up time were used as the learning qualities, and a model was built using SVM (Support Vector Machine) and linear regression algorithms [12; 13]. As a result of the experiment, a model was constructed in which the depression score was high due to irregular and short sleep times. Ref. [14] conducted research into a depression prediction model using the naive Bayes model. A total of 23 types of information, including sex, age, income, education, number of family members, marital status, stress perception, and education, were used as the learning qualities, and the model construction result showed an F-Measure of 0.974. Ref. [15] investigated a model that analyzes users' emotions based on images posted on Instagram. After analyzing the images using CNN-based models to extract emotions, a model was built to predict depression based on the extracted emotions. In [16], research was conducted using a method for predicting depression scores on the PHQ-9 scale using survey data provided by the National Health and Nutrition Examination Survey (NHANES) [7; 8]. The authors built a multi-class classification model based on the DNN algorithm, demonstrating an F1 score of 0.6, and in binary class classification, the F1 score was measured as 0.71 using a decision tree model. VAE is an algorithm that operates with the goal of extracting features that can sufficiently explain the input value X as one of the autoencoders and generating completely new data similar to X from the extracted features. Since VAE is based on a probabilistic model, flexible calculation is possible, but since it does not directly implement density, its performance may be lower than that of a model that does [17]. DAGMM (Deep Autoencoding Gaussian Mixture Model) is an algorithm studied based on GMM (Gaussian Mixture Model) and is a model in which Gaussian distribution is mixed. DAGMM uses an autoencoder to maintain the characteristics of the reduced dimension and restoration error, so that important information regarding the input value can be maintained even in the low dimension. Also, as a structure suitable for end-to-end learning, DAGMM does not fall into local optima [18]. COPOD is an unsupervised learning-based algorithm suitable for detecting outliers. COPOD analyzes the dependent structure between random variables and judges it as an outlier depending on whether it exceeds the threshold of the predicted outlier score. Therefore, it is an algorithm suitable for research that detects special situation changes [19]. LGBM is a tree-based learning algorithm with a gradient-boosting framework. In general algorithms, the tree expands horizontally, whereas in LGBM, the tree expands vertically, that is, LGBM is a leaf-wise form. To expand a tree, a leaf with maximum delta loss is selected, and when extending the same leaf, the leaf-wise algorithm is characterized by being able to reduce more loss than the level-wise algorithm. There are many studies that have analyzed time series data through LGBM; it has been applied in the prediction of the amount of wind power generated and in the prediction of missing values of air pollution information data.