Prediction of personality for mental health detection using hybrid deep learning model

Millions of people all over the globe struggle with some form of mental illness, so it's clear that mental health is an integral part of overall health and well-being. Individuals with mental health problems have much better results when they are diagnosed and treated early on. Results from applying machine learning algorithms for the early diagnosis and treatment of mental health issues have been promising in recent years. This article explores two machine learning algorithms for mental health prediction: K-nearest neighbors (KNN) and Recurrent Neural Networks (RNN). Data on mental health patients' demographics, health, and long-term habits was included. Based on demographics and medical history, the KNN algorithm classified people as having or not having a mental health condition, and the RNN algorithm recognized early symptoms of mental health difficulties based on behavioral patterns over time. Age, gender, and a family history of mental disease were the most critical indicators of KNN's 96.6% mental health prediction accuracy. Changes in sleeping and social interactions were the best predictors of mental health problems, which RNN detected 78.9% of the time. Machine learning can diagnose and treat mental health disorders early, according to one study. The study emphasizes the need for diverse and high-quality datasets for machine learning systems.