Overview / Usage

This project encompasses the creation of an advanced depression prediction system, centered around text analysis. It addresses the crucial challenge of identifying depression in individuals by analyzing their typed statements. By training the model on a substantial dataset containing expressions commonly associated with depression, the system learns to distinguish linguistic patterns indicative of depressive states. When presented with an input statement, the model gauges its similarity to known depressive phrases. Upon detecting a significant alignment, the system classifies the input as indicative of depression.

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The significance of this work lies in its potential to revolutionize mental health assessment and intervention. By automating the detection process, the system offers a swift, objective, and scalable means of identifying individuals at risk of depression. This technology could be seamlessly integrated into various platforms, such as mental health applications, social media monitoring tools, or counseling services. Its real-world applications encompass early intervention, efficient resource allocation, and personalized support for those in need. Ultimately, this research holds the promise of significantly improving mental health outcomes by enabling timely responses based on accurate predictive insights derived from text analysis.

Methodology / Approach

Our methodology encompasses a comprehensive approach to depression prediction and support, leveraging state-of-the-art techniques and technology. Here's an overview of our process:

Data Collection and Preprocessing: We curate a diverse dataset of text statements reflecting various emotional states, particularly those associated with depression. Preprocessing involves lemmatization, stemming, removal of stop words, lowercase conversion, and punctuation removal. This ensures standardized and meaningful text representations.

Feature Extraction and Transformation: Using TF-IDF, we convert preprocessed text data into numerical vectors, capturing essential semantic information while eliminating noise.

Random Forest Classifier: We employ the Random Forest Classifier, a robust ensemble learning algorithm. Trained on our processed dataset, this classifier excels in capturing intricate relationships within the data, enabling accurate depression prediction.

Intel OneAPI Integration: Leveraging Intel OneAPI's parallel computing capabilities, we optimize the classifier's performance, enabling swift and efficient predictions.

Matching Algorithm and Categorization: A matching algorithm assesses the similarity between user-input feelings and known depressive expressions. If a significant resemblance is detected, the input is categorized as indicative of depression.

Web Interface Development: We design a user-friendly web interface where individuals can input their feelings. The interface seamlessly integrates with the trained model, performing real-time analysis.

Results and Support: Based on the analysis, users receive immediate feedback. If depression is detected, the system provides empathetic guidance, directing users to helplines and suggesting coping mechanisms and resources to improve their well-being.

Deployment: The complete system is deployed as a user-friendly website, accessible to individuals seeking emotional support and insight. Users can confidentially input their emotions and gain valuable feedback and recommendations.

This methodology marries advanced text analysis, machine learning with Random Forest, and Intel OneAPI's computational prowess, culminating in a user-centric web application for depression prediction and support. By offering timely guidance and resources, we aspire to enhance mental health awareness and contribute positively to individuals' emotional well-being.

Repository

https://github.com/Kevin-Benny/IntelOneAPI_DepressionAnalysis

Collaborators