Wellminds Fordel - A Model Serving as a Foreman for Mental Health

Overview & Synopsis

The project proposes a novel approach to assess childhood depression severity through the integration of machine learning algorithms, specifically Support Vector Machines (SVM), and the Patient Health Questionnaire-9 (PHQ-9). Depression among children is a growing concern globally, yet its detection and severity assessment remain challenging. Leveraging the power of machine learning, this study aims to develop a predictive model capable of analyzing responses to a set of questions related to depressive symptoms and providing an output indicating the severity of depression. By training the model on a dataset comprising responses from children diagnosed with varying degrees of depression, including moderate and severe cases, the SVM algorithm, along with PHQ-9 and other relevant algorithms, will learn to recognize patterns indicative of different levels of depression severity. The output will categorize children's depression into levels such as moderate, mild, or severe, enabling early intervention and tailored support strategies. This project holds significant promise in augmenting traditional diagnostic methods with efficient, scalable, and objective assessment tools, facilitating timely interventions and ultimately improving the mental health outcomes of children worldwide. The Wellminds Fordel application not only determines if someone is suffering from depression but also identifies the specific type of depression they may be experiencing, recognizing that depression can manifest in various forms. This solution is intended for everyone, particularly individuals who may struggle to express their mental health concerns or choose to conceal them from others. By offering an accessible and discreet means of assessment, Wellminds Fordel aims to assist individuals in understanding and addressing their mental health issues effectively.