Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression

Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions individuals suffer from mental illnesses. It might be difficult identify those who are experiencing health illnesses provide them help that they need. Additionally, depression may associated thoughts suicide. Currently, there no clinically specific diagnostic biomarkers can severity type depression. In this research paper, novel particle swarm-cuckoo search (PS-CS) optimization algorithm proposed instead traditional backpropagation training deep neural networks. The widely used supervised learning in networks, but it has limitations terms convergence speed possibility getting trapped local optima. These problems were addressed by using network architecture detection tasks along PS-CS technique. combines strengths both swarm cuckoo algorithms, which allows more efficient effective parameters. We also evaluated how well suggested methods performed against most classification models, including (K-nearest neighbor) KNN, (support vector regression) SVR, decision trees, as residual (ResNet), visual geometry group (VGG), simple (LeNet). findings show method, PS-CS, conjunction CNN model, outperformed all other achieving maximum accuracy 99.5%. Other such logistic regression, achieved lower accuracies ranging 69% 97%.