# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 2 Literature Review  
[PERSON] et al. investigated the use of text analysis, emotional theory, machine learning, and linguistic processing techniques to assess an individual's state of depression by evaluating emotions in text from different platforms of social media [8]. [PERSON] et al. presented a method for more correctly recognising depression by transforming EEG characteristics and employing machine learning algorithms. An experiment was carried out utilising an emotional facial stimulus task, and EEG data from 28 volunteers were collected using a 128-channel HydroCel Geodesic Sensor Net (HCGSN) and analysed using Net Station software. To analyse the information, two separate methodologies, ensemble learning and deep learning, were utilised, and a support vector machine (SVM) was used as a classifier. They assessed the effectiveness of both techniques for single and total frequency bands. They discovered that utilising an ensemble model and power spectral density yielded the best accuracy of 89.02%, while deep learning approach and activity yielded the best accuracy of 84.75% [9]. Figure 1: The sequence of steps taken in the study’s process workflow. [PERSON] et al. examined a diagnostic strategy to distinguish cognitive biases in people with sub-clinical anxiety and depression. The study divided 125 people into four groups based on their symptoms, then used a behavioural test battery and powerful machine learning algorithms to detect and quantify several cognitive and emotional biases. The prediction model has a high accuracy of 71.44% in identifying individuals with severe depression or anxiety symptoms and 70.78% in identifying those without symptoms. The study also indicated which precise behavioural measurements were most essential in predicting group membership, shedding light on the cognitive mechanisms underlying anxiety and depression [10]. [PERSON] et al. gathered data from employed and unemployed persons of various nationalities and groups using the depressed, anxious, and perceived stress (DASS 21). They used five different machine learning algorithms to predict the intensity of anxiety, sadness, and stress and discovered an imbalance in the distribution of the classes in the findings. To overcome this problem, they used the F1 score measure, which allowed them determine that the random forest classifier had the highest accuracy of the five algorithms examined. Furthermore, the specificity parameter demonstrated that the algorithms were very good at properly recognizing negative findings [11]. By examining data from the online DASS42 tool, [PERSON] et al. used eight different machine learning algorithms to anticipate the occurrence of psychological disorders such as anxiety, depression, and stress. They predicted five different severity levels for each of these diseases and divided the systems into four categories: probabilistic, closest neighbour, neural network, and tree-based. In addition, to help in prediction, they used a composite classification algorithm. They also used the same techniques on a different dataset, DASS21, which they gathered themselves. The scientists observed that the hybrid algorithm produced higher prediction accuracy than a single method, but the radial basis function network, a sort of neural network, produced the best degree of accuracy [12]. [PERSON] et al. developed a strategy to predict depression and offer psychiatric care in order to obtain insight into the high incidence of depression among university students in Bangladesh, particularly undergraduates. After conferring with psychologists, counsellors, and professors, they polled students and utilised three algorithms to train and evaluate the dataset. They discovered that the random forest algorithm had the best accuracy for predicting depression, followed by the support vector machine approach, which had a comparable accuracy and f-measure of roughly 75% and 60%, respectively. The study's purpose was to detect depression early and ensure rapid recovery in order to avoid suicide [13]. [PERSON] et al.