Rapid Depression Detection Using Extreme Learning Machine: An AI-Driven Approach
Authors:-Assistant Professor Mrs.G.V.Rajeswari, Ch.Harikiran, K.L.Rishitha, K.H.Venkat Ganesh, B.Raj Kumar, V.L.Apoorva.
Abstract-Depression is one of the most prevalent psychological and mental health disorders, affecting a significant number of people worldwide. In recent years, Extreme Learning Machine (ELM) techniques have gained preference for addressing various health-related disease detection and prediction challenges. ELM is a single hidden layer feed-forward neural network (SLFN) that offers significantly faster convergence compared to traditional machine learning (ML) methods while delivering promising results. Although numerous studies have explored the application of ML models for depression detection, limited research has focused on utilizing ELM for this purpose. This study implements Extreme Learning Machine (ELM) alongside other ML techniques for depression detection, comparing their performance. The results demonstrate that ELM outperforms other methods, achieving the highest accuracy of 91.73%.
