## 2 Related Work  
The deep learning algorithm has reduced the feature selection method forecasting depression and it was not revealing the facts such as socio-lifestyle, economic, and demographics, and other risk factors to cause depression [13]. Predictions of depression can also be enhanced using unsupervised machine learning techniques, which help uncover hidden patterns and relationships within the data. A cluster solution is obtained using the self-organizing map, and the classification tasks are chosen from the clustered data to further evaluate the performance of the posterior probability multi-class SVM. A classification accuracy of 91.16% was achieved in this evaluation. Machine learning algorithms identify depressed groups by learning and recognizing underlying patterns in the data without predefined limits. \"Graphing lifestyle-environs using machine-learning methods\" (GLUMM) identified and characterized depression clusters using 96 factors from the NHANES (2009-2010). This study has faced the challenge of imbalanced data because the depressed participant's percentage is lower than the whole survey data [14]. The clinical diagnosis process for MDD normally encompasses the utilization of an interview-based methodology, an assessment of physical health, and, in particular cases, the carrying is carried out. XGBoost utilizes significant biomarkers, like HDL cholesterol and hemoglobin, associated with depression to forecast instances in a balanced sample, employing both over-sampling and under-sampling techniques to address dataset imbalances [15]. The multi-task learning (MTL) approach organizes the risk factors correlated with depression, and it is adaptable for choosing the subgroup level risk factors that are income range, hours of computer usage in the last 30 days, ethnicity, and living in different states [16]. In recent years, medical researchers have worked diligently to identify factors that increase the risk of depressive disorders. They have combined traditional statistical methods, like regression analysis, with modern data mining techniques to achieve this goal [17]. The logistic regression analysis results revealed a statistically marginal link between depression and the diabetes status of the family members. Furthermore, the trend analysis demonstrated that households with more diabetic family members were more likely to experience depression [18]. The preceding investigation examined data from a sample of 582 lonely older adult females in South Korea to comprehend and mitigate depression. The presence of obesity at the initial assessment was found to be correlated with a higher likelihood of experiencing depression [19]. A nomogram identifies the causing factors of depression in individuals at a heightened risk of anxiety, individual health, n-6 fatty acid, n-3 fatty acid, daily lack of movement, and sleep duration [20]. Research has explored machine learning to analyze heterogeneous data and examine quality of life and depression. The secure hash algorithm with an unsupervised and supervised model identified depressive factors with a 91.16% accuracy [21]. Information about participants' health, social life, and mental state was collected to predict depression. However, the quality of data collection has led to a large number of variables and challenges in selecting suitable analysis methods [14]. Research has investigated the relationship between psychosocial factors and mental health for earlier MDD diagnosis. The Short Form Survey-20 (SF-20) health-related quality of life has six subsets to analyze depression. However, the importance of features that are mostly related to depression was not reported [9]. The MRMR feature selection method identifies the sensitive features among 20 features (Table 1) and forecasts with a boosting mechanism. In this case, the feature subset is focused on only the CES-D (Center for Epidemiologic Studies Depression Scale) factor not considering the economic, general, health status, and socio-lifestyle [22].