Comparative Analysis for Prediction and Classification of Mental Health Issues and Challenges Using Hybrid Learning Techniques

Abstract

Mental health condition including sadness, anxiety, and sleep deprivation owns up to the emotional stress in young children, teens, and also in adults. It affects how a person thinks, ponders, feels, or responds to a certain circumstance or situation. Only if one has both good physical health and mental health, an individual can work productively and reach their full potential. Mental health is very important at every stage of life, from childhood to adulthood. We gathered information from online datasets that were readily available. For better prediction, the data has been labe-lencoded. To obtain labels, the data is subjected to several machine-learning approaches. The model that will be developed to forecast a person’s mental health will then be based on these categorized labels. Working-class individuals over the age of 1S are our target market. The model will be implemented into a website when it is created so that it may forecast the outcome based on the information provided by the user.