Enhancing Mental Disorder Diagnosis with Ensemble Bagging and Random Forest Techniques

The field of mental health diagnostics is drastically transformed with the emergence of artificial intelligence especially deep learning. This article explores the use of deep learning in mental health diagnosis by providing an overview of the state-of-the-art techniques that use ensemble methods to analyse intricate patterns in data. Our examination covers a range of mental health conditions, such as sleep disorder, mood swings, bipolar disorder, and anorexia, to show how ensemble learning performs better than conventional diagnostic methods in terms of accuracy, efficiency, and the ability to detect early-stage stage of mental health issues. The performance of the proposed model is compared with the state-of-the-art Machine Learning techniques such as multi-layer perceptron, bayes net, bagging and random forest. The paper also highlights ongoing research efforts and future directions in integrating deep learning with clinical practices for mental health diagnosis. By harnessing the power of ensemble learning, we envision a future where mental health diagnostics are more accessible, precise, and personalized, ultimately improving outcomes for individuals with mental disorders.