Description

Depression prediction is the use of machine learning algorithms to identify individuals who are at risk of developing depression or who are already depressed. This can be done by analyzing a variety of data sources. Depression prediction is important because it can help to improve early detection and intervention. The importance of an automatic approach to detect depression and determining its severity is very important. The early detection of depression-related symptoms could help to prevent suicides and deaths from depression. By identifying people who are at risk of developing depression, we can provide them with the support and resources they need to prevent the condition from becoming more severe. There are a number of different machine learning algorithms that can be used for depression prediction. In this we proposes hybrid machine learning algorithms such as LR and LSTM for depression prediction. The results show that the proposed approach outperforms traditional depression detection methods, such as SVM and random forests.