# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 2 Literature Review
investigated if voice might be used as a biomarker to diagnose minor and serious depression. The 93 individuals were separated into three groups: not depressed, moderate depressive episode, and severe depressive episode. The study team took 21 voice traits from semi-structured interview recordings and used analysis of variance to compare the three groups. Seven voice markers were found to differ across the three groups, even after age, BMI, and medication were taken into account. They discovered that a multi-layer processing method offered the best outcomes, with an AUC score of 65.9%, a sensitivity of 65.6%, and a specificity of 66.2% as a result. The voice patterns of people who were suffering depressive episodes and those who were not also showed significant changes, according to the study. Furthermore, their research demonstrated that it was able to distinguish between people who had minor and significant depression as well as those who were not depressed with accuracy when using machine learning. According to the study's findings, it was the first of its type to look into changes in speech patterns among those who had mild depression. According to the study's findings, speech analysis may be able to identify mild depression [14]. [PERSON] et al. investigated the efficiency of several categorization approaches in discriminating between those with and without depression. In their investigation, they used the power spectrum of three frequency bands (alpha, beta, and theta) as well as the complete EEG. According to the findings of their study, utilizing a support vector machine (SVM) classifier in conjunction with a genetic algorithm for feature selection provided an accuracy rate of 88.6% in categorizing depression patients [15]. [PERSON] et al. used machine learning algorithms and data from postpartum mothers collected from seven hospitals in Spain to construct predictive models for predicting the risk of postpartum depression (PPD). They used a holdout tactic to perform an internal review. They also created a simple flowchart and architecture for building the m-health app's graphical user interface. As a prediction model for PPD within the first week following delivery, the Naive Bayes algorithm was shown to be the most successful in terms of sensitivity and specificity [16]. [PERSON] et al. sought to enhance depression prediction by creating and employing machine learning approaches. They employed supervised machine learning to build a compact model that gives class labels based on a set of realistic features. The classification approach was used to give class labels to test individuals based on known predictive feature values, but the class label was unknown. They employed cutting-edge supervised learning classifiers with data adjustments. The findings were encouraging, demonstrating that machine learning might be used to successfully predict depression in type 2 diabetes patients [17]. [PERSON] et al. used discharge summaries to identify patients with depression using the MTERMS natural language processing (NLP) system and machine learning classification methods. Domain experts were invited to examine both the training and test instances, and these cases were classed as depressed with high, intermediate, and low confidence. All of the methods they examined performed similarly for cases of depression with high confidence, with MTERMS' knowledge-based decision tree slightly beating the machine learning classifiers, reaching an F-measure of 89.6%. MTERMS also fared the best on intermediate confidence instances (70.6% F-measure). With an F-measure of 70.0%, the RIPPER rule learner was the most successful machine learning approach, with greater accuracy but poorer recall than MTERMS. The suggested NLP-based technique proved successful in finding a considerable number of depression cases (about 20%) that were not on the coded diagnostic list [18]. [PERSON] et al.