Classification Model of Depression Based on the CNN-LSTM Network

Depression is a common psychological disorder, which can be extremely damaging people both physically and psychologically. A rapid and accurate programme of review of depressed mood is therefore important. This project used the Depression Rest dataset for 2 classifications of depression, including none and depression. A hybrid CNN-LSTM network was used, combining the advantages of convolutional neural networks and long and short-term memory networks, which is good for processing both sequential and temporal data. After tuning and training, the test set achieved an average classification accuracy of 95.5% with excellent performance. The research significance of this project is that depression is a common psychological disorder, but it is difficult to diagnose with subjective observation. Big data analysis and machine learning techniques to accurately determine the degree of depression can provide strong support for early screening, diagnosis and treatment of depression. The study also provides a valuable reference for future automated testing of depression levels based on biomarkers.