Mental Health Prediction Model in Eswatini Using Machine Learning Techniques

Résumé fourni par la source

Mental health is a very important aspect of our daily life because it includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. Mental and physical health are equally important components of overall health. Depression increases the risk for many types of physical health problems, particularly long-lasting conditions like diabetes, heart disease, and stroke. Similarly, the presence of chronic conditions can increase the risk of mental illness. In this study, we performed an analysis on the Eswatini Ministry of Health Dataset and discovered interesting patterns on mental health in the Kingdom of Eswatini. A machine learning models were developed for predicting anxiety and depression using the given features in the dataset. From this, a conclusion was drawn after comparing the findings from this study with the existing literature from an international perspective on predicting anxiety and depression. The models are compared based on their ability to train with the lowest error values. Streamlit was used to build the application that runs the model. This study concludes that early detection, such as identifying individuals at risk of developing anxiety or depression, can enable early intervention and prevent the progression of symptoms.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.