Developing an Efficient Text Prediction Model for Depression Detection using Intel OneAPI. Detecting depression from textual content has become increasingly important for early intervention and support. This problem statement focuses on creating a robust and accurate text prediction model using the Intel OneAPI toolkit to identify signs of depression within written text. And approach and assist people to help them.
Depression is a widespread mental health concern that affects millions of people globally. Timely detection and intervention are pivotal for effective treatment and support. Traditional methods of diagnosing depression often rely on self-reporting, which might not be accurate due to stigma or lack of awareness. Hence, there's a pressing need for non-invasive, scalable, and accurate tools that can identify individuals at risk of depression based on their language patterns.
- Random Forest Classification --> 93%
This research pioneers a depression prediction model utilizing Intel OneAPI and the Random Forest Classifier. By amalgamating biological, psychological, and sociocultural predictors, it aims to discern and forecast depressive tendencies with precision. Harnessing the parallel computing prowess of Intel OneAPI, the model ensures optimal performance for early detection and continuous monitoring, providing valuable insights for timely healthcare interventions. The study delves into feature importance analysis within the Random Forest framework, capitalizing on Intel OneAPI's capabilities to enhance variable scrutiny. Not only does this research contribute to heightened accuracy in depression prediction, but it also underscores the transformative potential of Intel OneAPI in reshaping mental health analytics. By advancing our comprehension of depression's multifaceted nature, this work endeavors to facilitate improved well-being and intervention strategies, marking a significant step forward in mental health research and technology.
